From 57d6482a7961472ec6f1c6ede2979e3ed48f1094 Mon Sep 17 00:00:00 2001 From: marcoyang1998 <45973641+marcoyang1998@users.noreply.github.com> Date: Fri, 21 Apr 2023 15:43:28 +0800 Subject: [PATCH] Streaming Zipformer with multi-dataset (#984) * modify train.py * add right padding option in decode.py * update RESULTS.md --- .../asr_datamodule.py | 10 +- egs/librispeech/ASR/.gitignore | 3 +- egs/librispeech/ASR/README.md | 1 + egs/librispeech/ASR/RESULTS.md | 140 +- .../pruned_transducer_stateless7/scaling.py | 5 +- .../export-for-ncnn.py | 1 - .../onnx_check.py | 3 +- .../streaming-ncnn-decode.py | 23 + .../test_model.py | 39 +- .../train.py | 24 +- .../asr_datamodule.py | 1 + .../beam_search.py | 1 + .../decode.py | 818 ++++++++++ .../decode_gigaspeech.py | 837 ++++++++++ .../decode_stream.py | 1 + .../decoder.py | 1 + .../encoder_interface.py | 1 + .../export-for-ncnn-zh.py | 1 + .../export-for-ncnn.py | 368 +++++ .../export-onnx.py | 1 + .../export.py | 1 + .../gigaspeech.py | 1 + .../gigaspeech_asrmodule.py | 1 + .../gigaspeech_scoring.py | 1 + .../jit_pretrained.py | 1 + .../jit_trace_export.py | 1 + .../jit_trace_pretrained.py | 1 + .../joiner.py | 1 + .../librispeech.py | 1 + .../model.py | 1 + .../onnx_check.py | 1 + .../onnx_model_wrapper.py | 1 + .../onnx_pretrained.py | 1 + .../optim.py | 1 + .../pretrained.py | 1 + .../scaling.py | 1 + .../scaling_converter.py | 1 + .../streaming-ncnn-decode.py | 1 + .../streaming_beam_search.py | 1 + .../streaming_decode.py | 610 ++++++++ .../test_model.py | 1 + .../train.py | 1370 +++++++++++++++++ .../train2.py | 1 + .../zipformer.py | 1 + .../zipformer2.py | 1 + 45 files changed, 4258 insertions(+), 24 deletions(-) create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/asr_datamodule.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/beam_search.py create mode 100755 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py create mode 100644 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_stream.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decoder.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/encoder_interface.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn-zh.py create mode 100755 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-onnx.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_asrmodule.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_scoring.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_pretrained.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_export.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_pretrained.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/joiner.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/librispeech.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/model.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_check.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_model_wrapper.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_pretrained.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/optim.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/pretrained.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling_converter.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming-ncnn-decode.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_beam_search.py create mode 100644 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_decode.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/test_model.py create mode 100755 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train2.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer.py create mode 120000 egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer2.py diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py index 5c01d7190f..4d5d2b8f91 100644 --- a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py @@ -387,14 +387,16 @@ def test_dataloaders(self, cuts: CutSet) -> DataLoader: @lru_cache() def train_cuts(self) -> CutSet: logging.info(f"About to get train_{self.args.subset} cuts") - path = self.args.manifest_dir / f"cuts_{self.args.subset}.jsonl.gz" + path = self.args.manifest_dir / f"gigaspeech_cuts_{self.args.subset}.jsonl.gz" cuts_train = CutSet.from_jsonl_lazy(path) return cuts_train @lru_cache() def dev_cuts(self) -> CutSet: logging.info("About to get dev cuts") - cuts_valid = load_manifest_lazy(self.args.manifest_dir / "cuts_DEV.jsonl.gz") + cuts_valid = load_manifest_lazy( + self.args.manifest_dir / "gigaspeech_cuts_DEV.jsonl.gz" + ) if self.args.small_dev: return cuts_valid.subset(first=1000) else: @@ -403,4 +405,6 @@ def dev_cuts(self) -> CutSet: @lru_cache() def test_cuts(self) -> CutSet: logging.info("About to get test cuts") - return load_manifest_lazy(self.args.manifest_dir / "cuts_TEST.jsonl.gz") + return load_manifest_lazy( + self.args.manifest_dir / "gigaspeech_cuts_TEST.jsonl.gz" + ) diff --git a/egs/librispeech/ASR/.gitignore b/egs/librispeech/ASR/.gitignore index 8dec2d86da..1c26f7978c 100644 --- a/egs/librispeech/ASR/.gitignore +++ b/egs/librispeech/ASR/.gitignore @@ -1,2 +1,3 @@ log-* -.DS_Store \ No newline at end of file +.DS_Store +run*.sh diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md index 9ffd78d5bf..82cef98173 100644 --- a/egs/librispeech/ASR/README.md +++ b/egs/librispeech/ASR/README.md @@ -26,6 +26,7 @@ The following table lists the differences among them. | `pruned_transducer_stateless7_ctc` | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but with extra CTC head| | `pruned_transducer_stateless7_ctc_bs` | Zipformer | Embedding + Conv1d | pruned_transducer_stateless7_ctc + blank skip | | `pruned_transducer_stateless7_streaming` | Streaming Zipformer | Embedding + Conv1d | streaming version of pruned_transducer_stateless7 | +| `pruned_transducer_stateless7_streaming_multi` | Streaming Zipformer | Embedding + Conv1d | same as pruned_transducer_stateless7_streaming, trained on LibriSpeech + GigaSpeech | | `pruned_transducer_stateless8` | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but using extra data from GigaSpeech| | `pruned_stateless_emformer_rnnt2` | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR| | `conv_emformer_transducer_stateless` | ConvEmformer | Embedding + Conv1d | Using ConvEmformer for streaming ASR + mechanisms in reworked model | diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md index 881e8be973..5a956fc9ce 100644 --- a/egs/librispeech/ASR/RESULTS.md +++ b/egs/librispeech/ASR/RESULTS.md @@ -1,5 +1,141 @@ ## Results +### Streaming Zipformer-Transducer (Pruned Stateless Transducer + Streaming Zipformer + Multi-Dataset) + +#### [pruned_transducer_stateless7_streaming_multi](./pruned_transducer_stateless7_streaming_multi) + +See for more details. + +You can find a pretrained model, training logs, decoding logs, and decoding +results at: + +Number of model parameters: 70369391, i.e., 70.37 M + +##### training on full librispeech + full gigaspeech (with giga_prob=0.9) + +The WERs are: + + +| decoding method | chunk size | test-clean | test-other | comment | decoding mode | +|----------------------|------------|------------|------------|---------------------|----------------------| +| greedy search | 320ms | 2.43 | 6.0 | --epoch 20 --avg 4 | simulated streaming | +| greedy search | 320ms | 2.47 | 6.13 | --epoch 20 --avg 4 | chunk-wise | +| fast beam search | 320ms | 2.43 | 5.99 | --epoch 20 --avg 4 | simulated streaming | +| fast beam search | 320ms | 2.8 | 6.46 | --epoch 20 --avg 4 | chunk-wise | +| modified beam search | 320ms | 2.4 | 5.96 | --epoch 20 --avg 4 | simulated streaming | +| modified beam search | 320ms | 2.42 | 6.03 | --epoch 20 --avg 4 | chunk-size | +| greedy search | 640ms | 2.26 | 5.58 | --epoch 20 --avg 4 | simulated streaming | +| greedy search | 640ms | 2.33 | 5.76 | --epoch 20 --avg 4 | chunk-wise | +| fast beam search | 640ms | 2.27 | 5.54 | --epoch 20 --avg 4 | simulated streaming | +| fast beam search | 640ms | 2.37 | 5.75 | --epoch 20 --avg 4 | chunk-wise | +| modified beam search | 640ms | 2.22 | 5.5 | --epoch 20 --avg 4 | simulated streaming | +| modified beam search | 640ms | 2.25 | 5.69 | --epoch 20 --avg 4 | chunk-size | + +The model also has good WERs on GigaSpeech. The following WERs are achieved on GigaSpeech test and dev sets: + +| decoding method | chunk size | dev | test | comment | decoding mode | +|----------------------|------------|-----|------|------------|---------------------| +| greedy search | 320ms | 12.08 | 11.98 | --epoch 20 --avg 4 | simulated streaming | +| greedy search | 640ms | 11.66 | 11.71 | --epoch 20 --avg 4 | simulated streaming | +| modified beam search | 320ms | 11.95 | 11.83 | --epoch 20 --avg 4 | simulated streaming | +| modified beam search | 320ms | 11.65 | 11.56 | --epoch 20 --avg 4 | simulated streaming | + + +Note: `simulated streaming` indicates feeding full utterance during decoding using `decode.py`, +while `chunk-size` indicates feeding certain number of frames at each time using `streaming_decode.py`. + +The training command is: + +```bash +./pruned_transducer_stateless7_streaming_multi/train.py \ + --world-size 4 \ + --num-epochs 20 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless7_streaming_multi/exp \ + --full-libri 1 \ + --giga-prob 0.9 \ + --max-duration 750 \ + --master-port 12345 +``` + +The tensorboard log can be found at + + +The simulated streaming decoding command (e.g., chunk-size=320ms) is: +```bash +for m in greedy_search fast_beam_search modified_beam_search; do + ./pruned_transducer_stateless7_streaming_multi/decode.py \ + --epoch 20 \ + --avg 4 \ + --exp-dir ./pruned_transducer_stateless7_streaming_multi/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --right-padding 64 \ + --decoding-method $m +done +``` + +The streaming chunk-size decoding command (e.g., chunk-size=320ms) is: +```bash +for m in greedy_search modified_beam_search fast_beam_search; do + ./pruned_transducer_stateless7_streaming_multi/streaming_decode.py \ + --epoch 20 \ + --avg 4 \ + --exp-dir ./pruned_transducer_stateless7_streaming_multi/exp \ + --decoding-method $m \ + --decode-chunk-len 32 \ + --num-decode-streams 2000 +done +``` + + +#### Smaller model + +We also provide a very small version (only 6.1M parameters) of this setup. The training command for the small model is: + +```bash +./pruned_transducer_stateless7_streaming_multi/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless7_streaming_multi/exp \ + --full-libri 1 \ + --giga-prob 0.9 \ + --num-encoder-layers "2,2,2,2,2" \ + --feedforward-dims "256,256,512,512,256" \ + --nhead "4,4,4,4,4" \ + --encoder-dims "128,128,128,128,128" \ + --attention-dims "96,96,96,96,96" \ + --encoder-unmasked-dims "96,96,96,96,96" \ + --max-duration 1200 \ + --master-port 12345 +``` + +You can find this pretrained small model and its training logs, decoding logs, and decoding +results at: + + + +| decoding method | chunk size | test-clean | test-other | comment | decoding mode | +|----------------------|------------|------------|------------|---------------------|----------------------| +| greedy search | 320ms | 5.95 | 15.03 | --epoch 30 --avg 1 | simulated streaming | +| greedy search | 640ms | 5.61 | 13.86 | --epoch 30 --avg 1 | simulated streaming | +| modified beam search | 320ms | 5.72 | 14.34 | --epoch 30 --avg 1 | simulated streaming | +| modified beam search | 640ms | 5.43 | 13.16 | --epoch 30 --avg 1 | simulated streaming | +| fast beam search | 320ms | 5.88 | 14.45 | --epoch 30 --avg 1 | simulated streaming | +| fast beam search | 640ms | 5.48 | 13.31 | --epoch 30 --avg 1 | simulated streaming | + +This small model achieves the following WERs on GigaSpeech test and dev sets: + +| decoding method | chunk size | dev | test | comment | decoding mode | +|----------------------|------------|------------|------------|---------------------|----------------------| +| greedy search | 320ms | 17.57 | 17.2 | --epoch 30 --avg 1 | simulated streaming | +| modified beam search | 320ms | 16.98 | 11.98 | --epoch 30 --avg 1 | simulated streaming | + +You can find the tensorboard logs at . + ### Streaming Zipformer-Transducer (Pruned Stateless Transducer + Streaming Zipformer) #### [pruned_transducer_stateless7_streaming](./pruned_transducer_stateless7_streaming) @@ -53,7 +189,7 @@ The tensorboard log can be found at The simulated streaming decoding command (e.g., chunk-size=320ms) is: ```bash -for $m in greedy_search fast_beam_search modified_beam_search; do +for m in greedy_search fast_beam_search modified_beam_search; do ./pruned_transducer_stateless7_streaming/decode.py \ --epoch 30 \ --avg 9 \ @@ -599,7 +735,7 @@ done ``` Note that a small change is made to the `pruned_transducer_stateless7/decoder.py` in -this [PR](/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_random_padding/egs/librispeech/ASR/pruned_transducer_stateless7/exp_960h_no_paddingidx_ngpu4/tensorboard) to address the +this [PR](https://github.com/k2-fsa/icefall/pull/942) to address the problem of emitting the first symbol at the very beginning. If you need a model without this issue, please download the model from here: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py index 156b91f093..30a7370619 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py @@ -777,8 +777,9 @@ def forward(ctx, x: Tensor, y: Tensor): @staticmethod def backward(ctx, ans_grad: Tensor): - return ans_grad, torch.ones( - ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device + return ( + ans_grad, + torch.ones(ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device), ) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py index 0f84eca835..f5589d1b2b 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py @@ -33,7 +33,6 @@ --use-averaged-model 0 \ --epoch 99 \ --avg 1 \ - \ --decode-chunk-len 32 \ --num-encoder-layers "2,4,3,2,4" \ --feedforward-dims "1024,1024,2048,2048,1024" \ diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_check.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_check.py index 6c78ba70b6..d7a4b95511 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_check.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_check.py @@ -71,13 +71,12 @@ import argparse import logging +import torch from onnx_pretrained import OnnxModel from zipformer import stack_states from icefall import is_module_available -import torch - def get_parser(): parser = argparse.ArgumentParser( diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py index 5a36b695ff..8acace9796 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py @@ -109,6 +109,29 @@ def __init__(self, args): self.init_joiner(args) # Please change the parameters according to your model + + # 20M + # self.num_encoder_layers = to_int_tuple("2,2,2,2,2") + # self.encoder_dims = to_int_tuple("256,256,256,256,256") # also known as d_model + # self.attention_dims = to_int_tuple("192,192,192,192,192") + # self.zipformer_downsampling_factors = to_int_tuple("1,2,4,8,2") + # self.cnn_module_kernels = to_int_tuple("31,31,31,31,31") + + # 9.6M + # self.num_encoder_layers = to_int_tuple("2,3,2,2,3") + # self.encoder_dims = to_int_tuple("160,160,160,160,160") # also known as d_model + # self.attention_dims = to_int_tuple("96,96,96,96,96") + # self.zipformer_downsampling_factors = to_int_tuple("1,2,4,8,2") + # self.cnn_module_kernels = to_int_tuple("31,31,31,31,31") + + # 5.5M or 6M + + # self.num_encoder_layers = to_int_tuple("2,2,2,2,2") + # self.encoder_dims = to_int_tuple("128,128,128,128,128") # also known as d_model + # self.attention_dims = to_int_tuple("96,96,96,96,96") + # self.zipformer_downsampling_factors = to_int_tuple("1,2,4,8,2") + # self.cnn_module_kernels = to_int_tuple("31,31,31,31,31") + self.num_encoder_layers = to_int_tuple("2,4,3,2,4") self.encoder_dims = to_int_tuple("384,384,384,384,384") # also known as d_model self.attention_dims = to_int_tuple("192,192,192,192,192") diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/test_model.py index 5400df804c..de12d7af13 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/test_model.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/test_model.py @@ -62,6 +62,43 @@ def test_model(): model = torch.jit.script(model) +def test_model_small(): + params = get_params() + params.vocab_size = 500 + params.blank_id = 0 + params.context_size = 2 + params.num_encoder_layers = "2,2,2,2,2" + params.feedforward_dims = "256,256,512,512,256" + params.nhead = "4,4,4,4,4" + params.encoder_dims = "128,128,128,128,128" + params.attention_dims = "96,96,96,96,96" + params.encoder_unmasked_dims = "96,96,96,96,96" + params.zipformer_downsampling_factors = "1,2,4,8,2" + params.cnn_module_kernels = "31,31,31,31,31" + params.decoder_dim = 320 + params.joiner_dim = 320 + params.num_left_chunks = 4 + params.short_chunk_size = 50 + params.decode_chunk_len = 32 + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + print(f"Number of model parameters: {num_param}") + import pdb + + pdb.set_trace() + + # Test jit script + convert_scaled_to_non_scaled(model, inplace=True) + # We won't use the forward() method of the model in C++, so just ignore + # it here. + # Otherwise, one of its arguments is a ragged tensor and is not + # torch scriptabe. + model.__class__.forward = torch.jit.ignore(model.__class__.forward) + print("Using torch.jit.script") + model = torch.jit.script(model) + + def test_model_jit_trace(): params = get_params() params.vocab_size = 500 @@ -142,7 +179,7 @@ def _test_joiner(): def main(): - test_model() + test_model_small() test_model_jit_trace() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py index c7a2a136da..b2f9ffc091 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py @@ -1049,10 +1049,10 @@ def run(rank, world_size, args): librispeech = LibriSpeechAsrDataModule(args) + train_cuts = librispeech.train_clean_100_cuts() if params.full_libri: - train_cuts = librispeech.train_all_shuf_cuts() - else: - train_cuts = librispeech.train_clean_100_cuts() + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() def remove_short_and_long_utt(c: Cut): # Keep only utterances with duration between 1 second and 20 seconds @@ -1091,7 +1091,7 @@ def remove_short_and_long_utt(c: Cut): return True - train_cuts = train_cuts.filter(remove_short_and_long_utt) + # train_cuts = train_cuts.filter(remove_short_and_long_utt) if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: # We only load the sampler's state dict when it loads a checkpoint @@ -1108,14 +1108,14 @@ def remove_short_and_long_utt(c: Cut): valid_cuts += librispeech.dev_other_cuts() valid_dl = librispeech.valid_dataloaders(valid_cuts) - if not params.print_diagnostics: - scan_pessimistic_batches_for_oom( - model=model, - train_dl=train_dl, - optimizer=optimizer, - sp=sp, - params=params, - ) + # if not params.print_diagnostics: + # scan_pessimistic_batches_for_oom( + # model=model, + # train_dl=train_dl, + # optimizer=optimizer, + # sp=sp, + # params=params, + # ) scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) if checkpoints and "grad_scaler" in checkpoints: diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/asr_datamodule.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/asr_datamodule.py new file mode 120000 index 0000000000..a3a1584d1a --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/asr_datamodule.py @@ -0,0 +1 @@ +../pruned_transducer_stateless8/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/beam_search.py new file mode 120000 index 0000000000..8554e44ccf --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py new file mode 100755 index 0000000000..35158ced4d --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py @@ -0,0 +1,818 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search (one best) +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless7_streaming/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --max-duration 600 \ + --decode-chunk-len 32 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 +""" + + +import argparse +import logging +import math +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import AsrDataModule +from beam_search import ( + beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from librispeech import LibriSpeech +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=9, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7_streaming/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--right-padding", + type=int, + default=64, + help="Padding frames at the end of features", + ) + + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + feature_lens += params.right_padding + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.right_padding), + value=LOG_EPS, + ) + encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens) + + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + word_table=word_table, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = ref_text.split() + this_batch.append((cut_id, ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_len}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + params.suffix += f"-right-padding-{params.right_padding}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + assert model.encoder.decode_chunk_size == params.decode_chunk_len // 2, ( + model.encoder.decode_chunk_size, + params.decode_chunk_len, + ) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + word_table = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + asr_datamodule = AsrDataModule(args) + librispeech = LibriSpeech(manifest_dir=args.manifest_dir) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_clean_dl = asr_datamodule.test_dataloaders(test_clean_cuts) + test_other_dl = asr_datamodule.test_dataloaders(test_other_cuts) + + test_sets = ["test-clean", "test-other"] + test_dl = [test_clean_dl, test_other_dl] + + for test_set, test_dl in zip(test_sets, test_dl): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + word_table=word_table, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py new file mode 100644 index 0000000000..a4f52ad7f0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py @@ -0,0 +1,837 @@ +#!/usr/bin/env python3 +# +# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang, +# Zengwei Yao, +# Xiaoyu Yang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: +(1) greedy search +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method greedy_search + +(2) beam search (not recommended) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 + +(3) modified beam search +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search (one best) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 + +(5) fast beam search (nbest) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(6) fast beam search (nbest oracle WER) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_oracle \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 \ + --num-paths 200 \ + --nbest-scale 0.5 + +(7) fast beam search (with LG) +./pruned_transducer_stateless7/decode.py \ + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless7/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search_nbest_LG \ + --beam 20.0 \ + --max-contexts 8 \ + --max-states 64 +""" + + +import argparse +import logging +import math +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import sentencepiece as spm +import torch +import torch.nn as nn +from beam_search import ( + beam_search, + fast_beam_search_nbest, + fast_beam_search_nbest_LG, + fast_beam_search_nbest_oracle, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) + +# from asr_datamodule import LibriSpeechAsrDataModule +from gigaspeech_asrmodule import GigaSpeechAsrDataModule +from gigaspeech_scoring import asr_text_post_processing +from train import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.lexicon import Lexicon +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=30, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 1. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=9, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--lang-dir", + type=Path, + default="data/lang_bpe_500", + help="The lang dir containing word table and LG graph", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Possible values are: + - greedy_search + - beam_search + - modified_beam_search + - fast_beam_search + - fast_beam_search_nbest + - fast_beam_search_nbest_oracle + - fast_beam_search_nbest_LG + If you use fast_beam_search_nbest_LG, you have to specify + `--lang-dir`, which should contain `LG.pt`. + """, + ) + + parser.add_argument( + "--beam-size", + type=int, + default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=20.0, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search, + fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle + """, + ) + + parser.add_argument( + "--ngram-lm-scale", + type=float, + default=0.01, + help=""" + Used only when --decoding_method is fast_beam_search_nbest_LG. + It specifies the scale for n-gram LM scores. + """, + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=64, + help="""Used only when --decoding-method is + fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG, + and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + parser.add_argument( + "--max-sym-per-frame", + type=int, + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", + ) + + parser.add_argument( + "--num-paths", + type=int, + default=200, + help="""Number of paths for nbest decoding. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--nbest-scale", + type=float, + default=0.5, + help="""Scale applied to lattice scores when computing nbest paths. + Used only when the decoding method is fast_beam_search_nbest, + fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""", + ) + + parser.add_argument( + "--decode-chunk-size", + type=int, + default=16, + help="The chunk size for decoding (in frames after subsampling)", + ) + + parser.add_argument( + "--right-padding", + type=int, + default=64, + help="Padding frames at the end of features", + ) + + add_model_arguments(parser) + + return parser + + +def post_processing( + results: List[Tuple[str, List[str], List[str]]], +) -> List[Tuple[str, List[str], List[str]]]: + new_results = [] + for key, ref, hyp in results: + new_ref = asr_text_post_processing(" ".join(ref)).split() + new_hyp = asr_text_post_processing(" ".join(hyp)).split() + new_results.append((key, new_ref, new_hyp)) + return new_results + + +def decode_one_batch( + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + batch: dict, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[List[str]]]: + """Decode one batch and return the result in a dict. The dict has the + following format: + + - key: It indicates the setting used for decoding. For example, + if greedy_search is used, it would be "greedy_search" + If beam search with a beam size of 7 is used, it would be + "beam_7" + - value: It contains the decoding result. `len(value)` equals to + batch size. `value[i]` is the decoding result for the i-th + utterance in the given batch. + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + batch: + It is the return value from iterating + `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation + for the format of the `batch`. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return the decoding result. See above description for the format of + the returned dict. + """ + device = next(model.parameters()).device + feature = batch["inputs"] + assert feature.ndim == 3 + + feature = feature.to(device) + # at entry, feature is (N, T, C) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + feature_lens += params.right_padding + feature = torch.nn.functional.pad( + feature, + pad=(0, 0, 0, params.right_padding), + value=LOG_EPS, + ) + encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens) + + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_LG": + hyp_tokens = fast_beam_search_nbest_LG( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in hyp_tokens: + hyps.append([word_table[i] for i in hyp]) + elif params.decoding_method == "fast_beam_search_nbest": + hyp_tokens = fast_beam_search_nbest( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "fast_beam_search_nbest_oracle": + hyp_tokens = fast_beam_search_nbest_oracle( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + num_paths=params.num_paths, + ref_texts=sp.encode(supervisions["text"]), + nbest_scale=params.nbest_scale, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1: + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + else: + batch_size = encoder_out.size(0) + + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyps.append(sp.decode(hyp).split()) + + if params.decoding_method == "greedy_search": + return {"greedy_search": hyps} + elif "fast_beam_search" in params.decoding_method: + key = f"beam_{params.beam}_" + key += f"max_contexts_{params.max_contexts}_" + key += f"max_states_{params.max_states}" + if "nbest" in params.decoding_method: + key += f"_num_paths_{params.num_paths}_" + key += f"nbest_scale_{params.nbest_scale}" + if "LG" in params.decoding_method: + key += f"_ngram_lm_scale_{params.ngram_lm_scale}" + + return {key: hyps} + else: + return {f"beam_size_{params.beam_size}": hyps} + + +def decode_dataset( + dl: torch.utils.data.DataLoader, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + word_table: Optional[k2.SymbolTable] = None, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[str, List[str], List[str]]]]: + """Decode dataset. + + Args: + dl: + PyTorch's dataloader containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + word_table: + The word symbol table. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search, fast_beam_search_nbest, + fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + num_cuts = 0 + + try: + num_batches = len(dl) + except TypeError: + num_batches = "?" + + if params.decoding_method == "greedy_search": + log_interval = 50 + else: + log_interval = 20 + + results = defaultdict(list) + for batch_idx, batch in enumerate(dl): + texts = batch["supervisions"]["text"] + cut_ids = [cut.id for cut in batch["supervisions"]["cut"]] + + hyps_dict = decode_one_batch( + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + word_table=word_table, + batch=batch, + ) + + for name, hyps in hyps_dict.items(): + this_batch = [] + assert len(hyps) == len(texts) + for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts): + ref_words = ref_text.split() + this_batch.append((cut_id, ref_words, hyp_words)) + + results[name].extend(this_batch) + + num_cuts += len(texts) + + if batch_idx % log_interval == 0: + batch_str = f"{batch_idx}/{num_batches}" + + logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}") + return results + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = ( + params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" + ) + results = post_processing(results) + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = ( + params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = ( + params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt" + ) + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + """ + This scripts test a libri model with libri BPE + on Gigaspeech. + """ + parser = get_parser() + GigaSpeechAsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + assert params.decoding_method in ( + "greedy_search", + "beam_search", + "fast_beam_search", + "fast_beam_search_nbest", + "fast_beam_search_nbest_LG", + "fast_beam_search_nbest_oracle", + "modified_beam_search", + ) + params.res_dir = params.exp_dir / (params.decoding_method + "_gigaspeech") + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_len}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + params.suffix += f"-right-padding-{params.right_padding}" + if "nbest" in params.decoding_method: + params.suffix += f"-nbest-scale-{params.nbest_scale}" + params.suffix += f"-num-paths-{params.num_paths}" + if "LG" in params.decoding_method: + params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}" + elif "beam_search" in params.decoding_method: + params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}" + else: + params.suffix += f"-context-{params.context_size}" + params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and are defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + + if "fast_beam_search" in params.decoding_method: + if params.decoding_method == "fast_beam_search_nbest_LG": + lexicon = Lexicon(params.lang_dir) + word_table = lexicon.word_table + lg_filename = params.lang_dir / "LG.pt" + logging.info(f"Loading {lg_filename}") + decoding_graph = k2.Fsa.from_dict( + torch.load(lg_filename, map_location=device) + ) + decoding_graph.scores *= params.ngram_lm_scale + else: + word_table = None + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + word_table = None + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + # we need cut ids to display recognition results. + args.return_cuts = True + gigaspeech = GigaSpeechAsrDataModule(args) + + dev_cuts = gigaspeech.dev_cuts() + test_cuts = gigaspeech.test_cuts() + + dev_dl = gigaspeech.test_dataloaders(dev_cuts) + test_dl = gigaspeech.test_dataloaders(test_cuts) + + test_sets = ["dev", "test"] + test_dls = [dev_dl, test_dl] + + for test_set, test_dl in zip(test_sets, test_dls): + results_dict = decode_dataset( + dl=test_dl, + params=params, + model=model, + sp=sp, + word_table=word_table, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_stream.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_stream.py new file mode 120000 index 0000000000..2b4596e0ba --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_stream.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/decode_stream.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decoder.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decoder.py new file mode 120000 index 0000000000..4e79a10e02 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decoder.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/decoder.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/encoder_interface.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/encoder_interface.py new file mode 120000 index 0000000000..24f414dd1c --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/encoder_interface.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/encoder_interface.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn-zh.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn-zh.py new file mode 120000 index 0000000000..c0e71accf9 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn-zh.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn.py new file mode 100755 index 0000000000..f5589d1b2b --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-for-ncnn.py @@ -0,0 +1,368 @@ +#!/usr/bin/env python3 + +""" +Please see +https://k2-fsa.github.io/icefall/model-export/export-ncnn.html +for more details about how to use this file. + +We use +https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29 +to demonstrate the usage of this file. + +1. Download the pre-trained model + +cd egs/librispeech/ASR + +repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29 +GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url +repo=$(basename $repo_url) + +pushd $repo +git lfs pull --include "data/lang_bpe/bpe.model" +git lfs pull --include "exp/pretrained.pt" + +cd exp +ln -s pretrained.pt epoch-99.pt +popd + +2. Export to ncnn + +./pruned_transducer_stateless7_streaming/export-for-ncnn.py \ + --bpe-model $repo/data/lang_bpe_500/bpe.model \ + --exp-dir $repo/exp \ + --use-averaged-model 0 \ + --epoch 99 \ + --avg 1 \ + --decode-chunk-len 32 \ + --num-encoder-layers "2,4,3,2,4" \ + --feedforward-dims "1024,1024,2048,2048,1024" \ + --nhead "8,8,8,8,8" \ + --encoder-dims "384,384,384,384,384" \ + --attention-dims "192,192,192,192,192" \ + --encoder-unmasked-dims "256,256,256,256,256" \ + --zipformer-downsampling-factors "1,2,4,8,2" \ + --cnn-module-kernels "31,31,31,31,31" \ + --decoder-dim 512 \ + --joiner-dim 512 + +cd $repo/exp + +pnnx encoder_jit_trace-pnnx.pt +pnnx decoder_jit_trace-pnnx.pt +pnnx joiner_jit_trace-pnnx.pt + +You can find converted models at +https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-en-2023-02-13 + +See ./streaming-ncnn-decode.py +and +https://github.com/k2-fsa/sherpa-ncnn +for usage. +""" + +import argparse +import logging +from pathlib import Path + +import sentencepiece as spm +import torch +from scaling_converter import convert_scaled_to_non_scaled +from train2 import add_model_arguments, get_params, get_transducer_model + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import setup_logger, str2bool + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7_streaming/exp", + help="""It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + add_model_arguments(parser) + + return parser + + +def export_encoder_model_jit_trace( + encoder_model: torch.nn.Module, + encoder_filename: str, +) -> None: + """Export the given encoder model with torch.jit.trace() + + Note: The warmup argument is fixed to 1. + + Args: + encoder_model: + The input encoder model + encoder_filename: + The filename to save the exported model. + """ + encoder_model.__class__.forward = encoder_model.__class__.streaming_forward + + decode_chunk_len = encoder_model.decode_chunk_size * 2 + pad_length = 7 + T = decode_chunk_len + pad_length # 32 + 7 = 39 + + logging.info(f"decode_chunk_len: {decode_chunk_len}") + logging.info(f"T: {T}") + + x = torch.zeros(1, T, 80, dtype=torch.float32) + states = encoder_model.get_init_state() + + traced_model = torch.jit.trace(encoder_model, (x, states)) + traced_model.save(encoder_filename) + logging.info(f"Saved to {encoder_filename}") + + +def export_decoder_model_jit_trace( + decoder_model: torch.nn.Module, + decoder_filename: str, +) -> None: + """Export the given decoder model with torch.jit.trace() + + Note: The argument need_pad is fixed to False. + + Args: + decoder_model: + The input decoder model + decoder_filename: + The filename to save the exported model. + """ + y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64) + need_pad = torch.tensor([False]) + + traced_model = torch.jit.trace(decoder_model, (y, need_pad)) + traced_model.save(decoder_filename) + logging.info(f"Saved to {decoder_filename}") + + +def export_joiner_model_jit_trace( + joiner_model: torch.nn.Module, + joiner_filename: str, +) -> None: + """Export the given joiner model with torch.jit.trace() + + Note: The argument project_input is fixed to True. A user should not + project the encoder_out/decoder_out by himself/herself. The exported joiner + will do that for the user. + + Args: + joiner_model: + The input joiner model + joiner_filename: + The filename to save the exported model. + + """ + encoder_out_dim = joiner_model.encoder_proj.weight.shape[1] + decoder_out_dim = joiner_model.decoder_proj.weight.shape[1] + encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32) + decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32) + + traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out)) + traced_model.save(joiner_filename) + logging.info(f"Saved to {joiner_filename}") + + +@torch.no_grad() +def main(): + args = get_parser().parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + device = torch.device("cpu") + + setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn") + + logging.info(f"device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if i >= 1: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to("cpu") + model.eval() + + convert_scaled_to_non_scaled(model, inplace=True, is_pnnx=True) + + encoder_num_param = sum([p.numel() for p in model.encoder.parameters()]) + decoder_num_param = sum([p.numel() for p in model.decoder.parameters()]) + joiner_num_param = sum([p.numel() for p in model.joiner.parameters()]) + total_num_param = encoder_num_param + decoder_num_param + joiner_num_param + logging.info(f"encoder parameters: {encoder_num_param}") + logging.info(f"decoder parameters: {decoder_num_param}") + logging.info(f"joiner parameters: {joiner_num_param}") + logging.info(f"total parameters: {total_num_param}") + + logging.info("Using torch.jit.trace()") + + logging.info("Exporting encoder") + encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt" + export_encoder_model_jit_trace(model.encoder, encoder_filename) + + logging.info("Exporting decoder") + decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt" + export_decoder_model_jit_trace(model.decoder, decoder_filename) + + logging.info("Exporting joiner") + joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt" + export_joiner_model_jit_trace(model.joiner, joiner_filename) + + +if __name__ == "__main__": + formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" + + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-onnx.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-onnx.py new file mode 120000 index 0000000000..137fa8ceca --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export-onnx.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/export-onnx.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export.py new file mode 120000 index 0000000000..6a009311cd --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/export.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/export.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech.py new file mode 120000 index 0000000000..6c6b08d3f0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech.py @@ -0,0 +1 @@ +../pruned_transducer_stateless8/gigaspeech.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_asrmodule.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_asrmodule.py new file mode 120000 index 0000000000..54f18a4f04 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_asrmodule.py @@ -0,0 +1 @@ +../../../gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_scoring.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_scoring.py new file mode 120000 index 0000000000..fdfa6ce4bb --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/gigaspeech_scoring.py @@ -0,0 +1 @@ +../../../gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_pretrained.py new file mode 120000 index 0000000000..c427e77097 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_pretrained.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/jit_pretrained.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_export.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_export.py new file mode 120000 index 0000000000..44ecf1780f --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_export.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/jit_trace_export.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_pretrained.py new file mode 120000 index 0000000000..762d38b73d --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/jit_trace_pretrained.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/jit_trace_pretrained.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/joiner.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/joiner.py new file mode 120000 index 0000000000..2a9c1ca5f5 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/joiner.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/joiner.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/librispeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/librispeech.py new file mode 120000 index 0000000000..7c22bc4b78 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/librispeech.py @@ -0,0 +1 @@ +../pruned_transducer_stateless8/librispeech.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/model.py new file mode 120000 index 0000000000..17ced2998e --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/model.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_check.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_check.py new file mode 120000 index 0000000000..8ed81ba1c0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_check.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/onnx_check.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_model_wrapper.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_model_wrapper.py new file mode 120000 index 0000000000..c780015d19 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_model_wrapper.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/onnx_model_wrapper.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_pretrained.py new file mode 120000 index 0000000000..da0236c2dc --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/onnx_pretrained.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/onnx_pretrained.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/optim.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/optim.py new file mode 120000 index 0000000000..6c5f3fc3e9 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/optim.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/optim.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/pretrained.py new file mode 120000 index 0000000000..4c519b7711 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/pretrained.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/pretrained.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling.py new file mode 120000 index 0000000000..420bc41497 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/scaling.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling_converter.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling_converter.py new file mode 120000 index 0000000000..b6cc7dc133 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/scaling_converter.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/scaling_converter.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming-ncnn-decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming-ncnn-decode.py new file mode 120000 index 0000000000..d137d28ade --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming-ncnn-decode.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_beam_search.py new file mode 120000 index 0000000000..dee9005d00 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_beam_search.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/streaming_beam_search.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_decode.py new file mode 100644 index 0000000000..78713f9205 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/streaming_decode.py @@ -0,0 +1,610 @@ +#!/usr/bin/env python3 +# Copyright 2022 Xiaomi Corporation (Authors: Wei Kang, Fangjun Kuang) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: +./pruned_transducer_stateless7_streaming/streaming_decode.py \ + --epoch 28 \ + --avg 15 \ + --decode-chunk-len 32 \ + --exp-dir ./pruned_transducer_stateless7_streaming/exp \ + --decoding_method greedy_search \ + --num-decode-streams 2000 +""" + +import argparse +import logging +import math +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import k2 +import numpy as np +import sentencepiece as spm +import torch +import torch.nn as nn +from asr_datamodule import AsrDataModule +from decode_stream import DecodeStream +from kaldifeat import Fbank, FbankOptions +from lhotse import CutSet +from librispeech import LibriSpeech +from streaming_beam_search import ( + fast_beam_search_one_best, + greedy_search, + modified_beam_search, +) +from torch.nn.utils.rnn import pad_sequence +from train import add_model_arguments, get_params, get_transducer_model +from zipformer import stack_states, unstack_states + +from icefall.checkpoint import ( + average_checkpoints, + average_checkpoints_with_averaged_model, + find_checkpoints, + load_checkpoint, +) +from icefall.utils import ( + AttributeDict, + setup_logger, + store_transcripts, + str2bool, + write_error_stats, +) + +LOG_EPS = math.log(1e-10) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--epoch", + type=int, + default=28, + help="""It specifies the checkpoint to use for decoding. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, + ) + + parser.add_argument( + "--avg", + type=int, + default=15, + help="Number of checkpoints to average. Automatically select " + "consecutive checkpoints before the checkpoint specified by " + "'--epoch' and '--iter'", + ) + + parser.add_argument( + "--use-averaged-model", + type=str2bool, + default=True, + help="Whether to load averaged model. Currently it only supports " + "using --epoch. If True, it would decode with the averaged model " + "over the epoch range from `epoch-avg` (excluded) to `epoch`." + "Actually only the models with epoch number of `epoch-avg` and " + "`epoch` are loaded for averaging. ", + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless2/exp", + help="The experiment dir", + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--decoding-method", + type=str, + default="greedy_search", + help="""Supported decoding methods are: + greedy_search + modified_beam_search + fast_beam_search + """, + ) + + parser.add_argument( + "--num_active_paths", + type=int, + default=4, + help="""An interger indicating how many candidates we will keep for each + frame. Used only when --decoding-method is modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=32, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--num-decode-streams", + type=int, + default=2000, + help="The number of streams that can be decoded parallel.", + ) + + add_model_arguments(parser) + + return parser + + +def decode_one_chunk( + params: AttributeDict, + model: nn.Module, + decode_streams: List[DecodeStream], +) -> List[int]: + """Decode one chunk frames of features for each decode_streams and + return the indexes of finished streams in a List. + + Args: + params: + It's the return value of :func:`get_params`. + model: + The neural model. + decode_streams: + A List of DecodeStream, each belonging to a utterance. + Returns: + Return a List containing which DecodeStreams are finished. + """ + device = model.device + + features = [] + feature_lens = [] + states = [] + processed_lens = [] + + for stream in decode_streams: + feat, feat_len = stream.get_feature_frames(params.decode_chunk_len) + features.append(feat) + feature_lens.append(feat_len) + states.append(stream.states) + processed_lens.append(stream.done_frames) + + feature_lens = torch.tensor(feature_lens, device=device) + features = pad_sequence(features, batch_first=True, padding_value=LOG_EPS) + + # We subsample features with ((x_len - 7) // 2 + 1) // 2 and the max downsampling + # factor in encoders is 8. + # After feature embedding (x_len - 7) // 2, we have (23 - 7) // 2 = 8. + tail_length = 23 + if features.size(1) < tail_length: + pad_length = tail_length - features.size(1) + feature_lens += pad_length + features = torch.nn.functional.pad( + features, + (0, 0, 0, pad_length), + mode="constant", + value=LOG_EPS, + ) + + states = stack_states(states) + processed_lens = torch.tensor(processed_lens, device=device) + + encoder_out, encoder_out_lens, new_states = model.encoder.streaming_forward( + x=features, + x_lens=feature_lens, + states=states, + ) + + encoder_out = model.joiner.encoder_proj(encoder_out) + + if params.decoding_method == "greedy_search": + greedy_search(model=model, encoder_out=encoder_out, streams=decode_streams) + elif params.decoding_method == "fast_beam_search": + processed_lens = processed_lens + encoder_out_lens + fast_beam_search_one_best( + model=model, + encoder_out=encoder_out, + processed_lens=processed_lens, + streams=decode_streams, + beam=params.beam, + max_states=params.max_states, + max_contexts=params.max_contexts, + ) + elif params.decoding_method == "modified_beam_search": + modified_beam_search( + model=model, + streams=decode_streams, + encoder_out=encoder_out, + num_active_paths=params.num_active_paths, + ) + else: + raise ValueError(f"Unsupported decoding method: {params.decoding_method}") + + states = unstack_states(new_states) + + finished_streams = [] + for i in range(len(decode_streams)): + decode_streams[i].states = states[i] + decode_streams[i].done_frames += encoder_out_lens[i] + if decode_streams[i].done: + finished_streams.append(i) + + return finished_streams + + +def decode_dataset( + cuts: CutSet, + params: AttributeDict, + model: nn.Module, + sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, +) -> Dict[str, List[Tuple[List[str], List[str]]]]: + """Decode dataset. + + Args: + cuts: + Lhotse Cutset containing the dataset to decode. + params: + It is returned by :func:`get_params`. + model: + The neural model. + sp: + The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. + Returns: + Return a dict, whose key may be "greedy_search" if greedy search + is used, or it may be "beam_7" if beam size of 7 is used. + Its value is a list of tuples. Each tuple contains two elements: + The first is the reference transcript, and the second is the + predicted result. + """ + device = model.device + + opts = FbankOptions() + opts.device = device + opts.frame_opts.dither = 0 + opts.frame_opts.snip_edges = False + opts.frame_opts.samp_freq = 16000 + opts.mel_opts.num_bins = 80 + + log_interval = 50 + + decode_results = [] + # Contain decode streams currently running. + decode_streams = [] + for num, cut in enumerate(cuts): + # each utterance has a DecodeStream. + initial_states = model.encoder.get_init_state(device=device) + decode_stream = DecodeStream( + params=params, + cut_id=cut.id, + initial_states=initial_states, + decoding_graph=decoding_graph, + device=device, + ) + + audio: np.ndarray = cut.load_audio() + # audio.shape: (1, num_samples) + assert len(audio.shape) == 2 + assert audio.shape[0] == 1, "Should be single channel" + assert audio.dtype == np.float32, audio.dtype + + # The trained model is using normalized samples + assert audio.max() <= 1, "Should be normalized to [-1, 1])" + + samples = torch.from_numpy(audio).squeeze(0) + + fbank = Fbank(opts) + feature = fbank(samples.to(device)) + decode_stream.set_features(feature, tail_pad_len=params.decode_chunk_len) + decode_stream.ground_truth = cut.supervisions[0].text + + decode_streams.append(decode_stream) + + while len(decode_streams) >= params.num_decode_streams: + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + decode_results.append( + ( + decode_streams[i].id, + decode_streams[i].ground_truth.split(), + sp.decode(decode_streams[i].decoding_result()).split(), + ) + ) + del decode_streams[i] + + if num % log_interval == 0: + logging.info(f"Cuts processed until now is {num}.") + + # decode final chunks of last sequences + while len(decode_streams): + finished_streams = decode_one_chunk( + params=params, model=model, decode_streams=decode_streams + ) + for i in sorted(finished_streams, reverse=True): + decode_results.append( + ( + decode_streams[i].id, + decode_streams[i].ground_truth.split(), + sp.decode(decode_streams[i].decoding_result()).split(), + ) + ) + del decode_streams[i] + + if params.decoding_method == "greedy_search": + key = "greedy_search" + elif params.decoding_method == "fast_beam_search": + key = ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ) + elif params.decoding_method == "modified_beam_search": + key = f"num_active_paths_{params.num_active_paths}" + else: + raise ValueError(f"Unsupported decoding method: {params.decoding_method}") + return {key: decode_results} + + +def save_results( + params: AttributeDict, + test_set_name: str, + results_dict: Dict[str, List[Tuple[List[str], List[str]]]], +): + test_set_wers = dict() + for key, results in results_dict.items(): + recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt" + results = sorted(results) + store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") + + # The following prints out WERs, per-word error statistics and aligned + # ref/hyp pairs. + errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt" + with open(errs_filename, "w") as f: + wer = write_error_stats( + f, f"{test_set_name}-{key}", results, enable_log=True + ) + test_set_wers[key] = wer + + logging.info("Wrote detailed error stats to {}".format(errs_filename)) + + test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1]) + errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt" + with open(errs_info, "w") as f: + print("settings\tWER", file=f) + for key, val in test_set_wers: + print("{}\t{}".format(key, val), file=f) + + s = "\nFor {}, WER of different settings are:\n".format(test_set_name) + note = "\tbest for {}".format(test_set_name) + for key, val in test_set_wers: + s += "{}\t{}{}\n".format(key, val, note) + note = "" + logging.info(s) + + +@torch.no_grad() +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + params = get_params() + params.update(vars(args)) + + params.res_dir = params.exp_dir / "streaming" / params.decoding_method + + if params.iter > 0: + params.suffix = f"iter-{params.iter}-avg-{params.avg}" + else: + params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" + + # for streaming + params.suffix += f"-streaming-chunk-size-{params.decode_chunk_len}" + + # for fast_beam_search + if params.decoding_method == "fast_beam_search": + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + + if params.use_averaged_model: + params.suffix += "-use-averaged-model" + + setup_logger(f"{params.res_dir}/log-decode-{params.suffix}") + logging.info("Decoding started") + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", 0) + + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # and is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.unk_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + if not params.use_averaged_model: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + elif params.avg == 1: + load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model) + else: + start = params.epoch - params.avg + 1 + filenames = [] + for i in range(start, params.epoch + 1): + if start >= 0: + filenames.append(f"{params.exp_dir}/epoch-{i}.pt") + logging.info(f"averaging {filenames}") + model.to(device) + model.load_state_dict(average_checkpoints(filenames, device=device)) + else: + if params.iter > 0: + filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[ + : params.avg + 1 + ] + if len(filenames) == 0: + raise ValueError( + f"No checkpoints found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + elif len(filenames) < params.avg + 1: + raise ValueError( + f"Not enough checkpoints ({len(filenames)}) found for" + f" --iter {params.iter}, --avg {params.avg}" + ) + filename_start = filenames[-1] + filename_end = filenames[0] + logging.info( + "Calculating the averaged model over iteration checkpoints" + f" from {filename_start} (excluded) to {filename_end}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + else: + assert params.avg > 0, params.avg + start = params.epoch - params.avg + assert start >= 1, start + filename_start = f"{params.exp_dir}/epoch-{start}.pt" + filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt" + logging.info( + f"Calculating the averaged model over epoch range from " + f"{start} (excluded) to {params.epoch}" + ) + model.to(device) + model.load_state_dict( + average_checkpoints_with_averaged_model( + filename_start=filename_start, + filename_end=filename_end, + device=device, + ) + ) + + model.to(device) + model.eval() + model.device = device + + decoding_graph = None + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + librispeech = LibriSpeech(manifest_dir=args.manifest_dir) + + test_clean_cuts = librispeech.test_clean_cuts() + test_other_cuts = librispeech.test_other_cuts() + + test_sets = ["test-clean", "test-other"] + test_cuts = [test_clean_cuts, test_other_cuts] + + for test_set, test_cut in zip(test_sets, test_cuts): + results_dict = decode_dataset( + cuts=test_cut, + params=params, + model=model, + sp=sp, + decoding_graph=decoding_graph, + ) + + save_results( + params=params, + test_set_name=test_set, + results_dict=results_dict, + ) + + logging.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/test_model.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/test_model.py new file mode 120000 index 0000000000..1a9ba93e6f --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/test_model.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/test_model.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py new file mode 100755 index 0000000000..09e8a512f0 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py @@ -0,0 +1,1370 @@ +#!/usr/bin/env python3 +# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang, +# Wei Kang, +# Mingshuang Luo,) +# Zengwei Yao) +# +# See ../../../../LICENSE for clarification regarding multiple authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Usage: + +export CUDA_VISIBLE_DEVICES="0,1,2,3" + +./pruned_transducer_stateless7_streaming/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --exp-dir pruned_transducer_stateless7_streaming/exp \ + --full-libri 1 \ + --max-duration 300 + +# For mix precision training: + +./pruned_transducer_stateless7_streaming/train.py \ + --world-size 4 \ + --num-epochs 30 \ + --start-epoch 1 \ + --use-fp16 1 \ + --exp-dir pruned_transducer_stateless7_streaming/exp \ + --full-libri 1 \ + --max-duration 550 +""" + + +import argparse +import copy +import logging +import random +import warnings +from pathlib import Path +from shutil import copyfile +from typing import Any, Dict, Optional, Tuple, Union + +import k2 +import optim +import sentencepiece as spm +import torch +import torch.multiprocessing as mp +import torch.nn as nn +from asr_datamodule import AsrDataModule +from decoder import Decoder +from gigaspeech import GigaSpeech +from joiner import Joiner +from lhotse import CutSet, load_manifest +from lhotse.cut import Cut +from lhotse.dataset.sampling.base import CutSampler +from lhotse.utils import fix_random_seed +from librispeech import LibriSpeech +from model import Transducer +from optim import Eden, ScaledAdam +from torch import Tensor +from torch.cuda.amp import GradScaler +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from zipformer import Zipformer + +from icefall import diagnostics +from icefall.checkpoint import load_checkpoint, remove_checkpoints +from icefall.checkpoint import save_checkpoint as save_checkpoint_impl +from icefall.checkpoint import ( + save_checkpoint_with_global_batch_idx, + update_averaged_model, +) +from icefall.dist import cleanup_dist, setup_dist +from icefall.env import get_env_info +from icefall.hooks import register_inf_check_hooks +from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool + +LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler] + + +def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None: + if isinstance(model, DDP): + # get underlying nn.Module + model = model.module + for module in model.modules(): + if hasattr(module, "batch_count"): + module.batch_count = batch_count + + +def add_model_arguments(parser: argparse.ArgumentParser): + parser.add_argument( + "--num-encoder-layers", + type=str, + default="2,4,3,2,4", + help="Number of zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--feedforward-dims", + type=str, + default="1024,1024,2048,2048,1024", + help="Feedforward dimension of the zipformer encoder layers, comma separated.", + ) + + parser.add_argument( + "--nhead", + type=str, + default="8,8,8,8,8", + help="Number of attention heads in the zipformer encoder layers.", + ) + + parser.add_argument( + "--encoder-dims", + type=str, + default="384,384,384,384,384", + help="Embedding dimension in the 2 blocks of zipformer encoder layers, comma separated", + ) + + parser.add_argument( + "--attention-dims", + type=str, + default="192,192,192,192,192", + help="""Attention dimension in the 2 blocks of zipformer encoder layers, comma separated; + not the same as embedding dimension.""", + ) + + parser.add_argument( + "--encoder-unmasked-dims", + type=str, + default="256,256,256,256,256", + help="Unmasked dimensions in the encoders, relates to augmentation during training. " + "Must be <= each of encoder_dims. Empirically, less than 256 seems to make performance " + " worse.", + ) + + parser.add_argument( + "--zipformer-downsampling-factors", + type=str, + default="1,2,4,8,2", + help="Downsampling factor for each stack of encoder layers.", + ) + + parser.add_argument( + "--cnn-module-kernels", + type=str, + default="31,31,31,31,31", + help="Sizes of kernels in convolution modules", + ) + + parser.add_argument( + "--decoder-dim", + type=int, + default=512, + help="Embedding dimension in the decoder model.", + ) + + parser.add_argument( + "--joiner-dim", + type=int, + default=512, + help="""Dimension used in the joiner model. + Outputs from the encoder and decoder model are projected + to this dimension before adding. + """, + ) + + parser.add_argument( + "--short-chunk-size", + type=int, + default=50, + help="""Chunk length of dynamic training, the chunk size would be either + max sequence length of current batch or uniformly sampled from (1, short_chunk_size). + """, + ) + + parser.add_argument( + "--num-left-chunks", + type=int, + default=4, + help="How many left context can be seen in chunks when calculating attention.", + ) + + parser.add_argument( + "--decode-chunk-len", + type=int, + default=32, + help="The chunk size for decoding (in frames before subsampling)", + ) + + +def get_parser(): + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter + ) + + parser.add_argument( + "--world-size", + type=int, + default=1, + help="Number of GPUs for DDP training.", + ) + + parser.add_argument( + "--master-port", + type=int, + default=12354, + help="Master port to use for DDP training.", + ) + + parser.add_argument( + "--tensorboard", + type=str2bool, + default=True, + help="Should various information be logged in tensorboard.", + ) + + parser.add_argument( + "--full-libri", + type=str2bool, + default=True, + help="When enabled, use 960h LibriSpeech. Otherwise, use 100h subset.", + ) + + parser.add_argument( + "--num-epochs", + type=int, + default=30, + help="Number of epochs to train.", + ) + + parser.add_argument( + "--start-epoch", + type=int, + default=1, + help="""Resume training from this epoch. It should be positive. + If larger than 1, it will load checkpoint from + exp-dir/epoch-{start_epoch-1}.pt + """, + ) + + parser.add_argument( + "--start-batch", + type=int, + default=0, + help="""If positive, --start-epoch is ignored and + it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt + """, + ) + + parser.add_argument( + "--exp-dir", + type=str, + default="pruned_transducer_stateless7_streaming/exp", + help="""The experiment dir. + It specifies the directory where all training related + files, e.g., checkpoints, log, etc, are saved + """, + ) + + parser.add_argument( + "--bpe-model", + type=str, + default="data/lang_bpe_500/bpe.model", + help="Path to the BPE model", + ) + + parser.add_argument( + "--base-lr", type=float, default=0.05, help="The base learning rate." + ) + + parser.add_argument( + "--lr-batches", + type=float, + default=5000, + help="""Number of steps that affects how rapidly the learning rate + decreases. We suggest not to change this.""", + ) + + parser.add_argument( + "--lr-epochs", + type=float, + default=3.5, + help="""Number of epochs that affects how rapidly the learning rate decreases. + """, + ) + + parser.add_argument( + "--context-size", + type=int, + default=2, + help="The context size in the decoder. 1 means bigram; 2 means tri-gram", + ) + + parser.add_argument( + "--prune-range", + type=int, + default=5, + help="The prune range for rnnt loss, it means how many symbols(context)" + "we are using to compute the loss", + ) + + parser.add_argument( + "--lm-scale", + type=float, + default=0.25, + help="The scale to smooth the loss with lm " + "(output of prediction network) part.", + ) + + parser.add_argument( + "--am-scale", + type=float, + default=0.0, + help="The scale to smooth the loss with am (output of encoder network) part.", + ) + + parser.add_argument( + "--simple-loss-scale", + type=float, + default=0.5, + help="To get pruning ranges, we will calculate a simple version" + "loss(joiner is just addition), this simple loss also uses for" + "training (as a regularization item). We will scale the simple loss" + "with this parameter before adding to the final loss.", + ) + + parser.add_argument( + "--seed", + type=int, + default=42, + help="The seed for random generators intended for reproducibility", + ) + + parser.add_argument( + "--print-diagnostics", + type=str2bool, + default=False, + help="Accumulate stats on activations, print them and exit.", + ) + + parser.add_argument( + "--inf-check", + type=str2bool, + default=False, + help="Add hooks to check for infinite module outputs and gradients.", + ) + + parser.add_argument( + "--save-every-n", + type=int, + default=2000, + help="""Save checkpoint after processing this number of batches" + periodically. We save checkpoint to exp-dir/ whenever + params.batch_idx_train % save_every_n == 0. The checkpoint filename + has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt' + Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the + end of each epoch where `xxx` is the epoch number counting from 0. + """, + ) + + parser.add_argument( + "--keep-last-k", + type=int, + default=30, + help="""Only keep this number of checkpoints on disk. + For instance, if it is 3, there are only 3 checkpoints + in the exp-dir with filenames `checkpoint-xxx.pt`. + It does not affect checkpoints with name `epoch-xxx.pt`. + """, + ) + + parser.add_argument( + "--average-period", + type=int, + default=200, + help="""Update the averaged model, namely `model_avg`, after processing + this number of batches. `model_avg` is a separate version of model, + in which each floating-point parameter is the average of all the + parameters from the start of training. Each time we take the average, + we do: `model_avg = model * (average_period / batch_idx_train) + + model_avg * ((batch_idx_train - average_period) / batch_idx_train)`. + """, + ) + + parser.add_argument( + "--use-fp16", + type=str2bool, + default=False, + help="Whether to use half precision training.", + ) + + parser.add_argument( + "--giga-prob", + type=float, + default=0.5, + help="The probability to select a batch from the GigaSpeech dataset", + ) + + add_model_arguments(parser) + + return parser + + +def get_params() -> AttributeDict: + """Return a dict containing training parameters. + + All training related parameters that are not passed from the commandline + are saved in the variable `params`. + + Commandline options are merged into `params` after they are parsed, so + you can also access them via `params`. + + Explanation of options saved in `params`: + + - best_train_loss: Best training loss so far. It is used to select + the model that has the lowest training loss. It is + updated during the training. + + - best_valid_loss: Best validation loss so far. It is used to select + the model that has the lowest validation loss. It is + updated during the training. + + - best_train_epoch: It is the epoch that has the best training loss. + + - best_valid_epoch: It is the epoch that has the best validation loss. + + - batch_idx_train: Used to writing statistics to tensorboard. It + contains number of batches trained so far across + epochs. + + - log_interval: Print training loss if batch_idx % log_interval` is 0 + + - reset_interval: Reset statistics if batch_idx % reset_interval is 0 + + - valid_interval: Run validation if batch_idx % valid_interval is 0 + + - feature_dim: The model input dim. It has to match the one used + in computing features. + + - subsampling_factor: The subsampling factor for the model. + + - encoder_dim: Hidden dim for multi-head attention model. + + - num_decoder_layers: Number of decoder layer of transformer decoder. + + - warm_step: The warmup period that dictates the decay of the + scale on "simple" (un-pruned) loss. + """ + params = AttributeDict( + { + "best_train_loss": float("inf"), + "best_valid_loss": float("inf"), + "best_train_epoch": -1, + "best_valid_epoch": -1, + "batch_idx_train": 0, + "log_interval": 50, + "reset_interval": 200, + "valid_interval": 3000, # For the 100h subset, use 800 + # parameters for zipformer + "feature_dim": 80, + "subsampling_factor": 4, # not passed in, this is fixed. + "warm_step": 2000, + "env_info": get_env_info(), + } + ) + + return params + + +def get_encoder_model(params: AttributeDict) -> nn.Module: + # TODO: We can add an option to switch between Zipformer and Transformer + def to_int_tuple(s: str): + return tuple(map(int, s.split(","))) + + encoder = Zipformer( + num_features=params.feature_dim, + output_downsampling_factor=2, + zipformer_downsampling_factors=to_int_tuple( + params.zipformer_downsampling_factors + ), + encoder_dims=to_int_tuple(params.encoder_dims), + attention_dim=to_int_tuple(params.attention_dims), + encoder_unmasked_dims=to_int_tuple(params.encoder_unmasked_dims), + nhead=to_int_tuple(params.nhead), + feedforward_dim=to_int_tuple(params.feedforward_dims), + cnn_module_kernels=to_int_tuple(params.cnn_module_kernels), + num_encoder_layers=to_int_tuple(params.num_encoder_layers), + num_left_chunks=params.num_left_chunks, + short_chunk_size=params.short_chunk_size, + decode_chunk_size=params.decode_chunk_len // 2, + ) + return encoder + + +def get_decoder_model(params: AttributeDict) -> nn.Module: + decoder = Decoder( + vocab_size=params.vocab_size, + decoder_dim=params.decoder_dim, + blank_id=params.blank_id, + context_size=params.context_size, + ) + return decoder + + +def get_joiner_model(params: AttributeDict) -> nn.Module: + joiner = Joiner( + encoder_dim=int(params.encoder_dims.split(",")[-1]), + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return joiner + + +def get_transducer_model(params: AttributeDict) -> nn.Module: + encoder = get_encoder_model(params) + decoder = get_decoder_model(params) + joiner = get_joiner_model(params) + + model = Transducer( + encoder=encoder, + decoder=decoder, + joiner=joiner, + encoder_dim=int(params.encoder_dims.split(",")[-1]), + decoder_dim=params.decoder_dim, + joiner_dim=params.joiner_dim, + vocab_size=params.vocab_size, + ) + return model + + +def load_checkpoint_if_available( + params: AttributeDict, + model: nn.Module, + model_avg: nn.Module = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, +) -> Optional[Dict[str, Any]]: + """Load checkpoint from file. + + If params.start_batch is positive, it will load the checkpoint from + `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if + params.start_epoch is larger than 1, it will load the checkpoint from + `params.start_epoch - 1`. + + Apart from loading state dict for `model` and `optimizer` it also updates + `best_train_epoch`, `best_train_loss`, `best_valid_epoch`, + and `best_valid_loss` in `params`. + + Args: + params: + The return value of :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer that we are using. + scheduler: + The scheduler that we are using. + Returns: + Return a dict containing previously saved training info. + """ + if params.start_batch > 0: + filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt" + elif params.start_epoch > 1: + filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt" + else: + return None + + assert filename.is_file(), f"{filename} does not exist!" + + saved_params = load_checkpoint( + filename, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + ) + + keys = [ + "best_train_epoch", + "best_valid_epoch", + "batch_idx_train", + "best_train_loss", + "best_valid_loss", + ] + for k in keys: + params[k] = saved_params[k] + + if params.start_batch > 0: + if "cur_epoch" in saved_params: + params["start_epoch"] = saved_params["cur_epoch"] + + if "cur_batch_idx" in saved_params: + params["cur_batch_idx"] = saved_params["cur_batch_idx"] + + return saved_params + + +def save_checkpoint( + params: AttributeDict, + model: Union[nn.Module, DDP], + model_avg: Optional[nn.Module] = None, + optimizer: Optional[torch.optim.Optimizer] = None, + scheduler: Optional[LRSchedulerType] = None, + sampler: Optional[CutSampler] = None, + scaler: Optional[GradScaler] = None, + rank: int = 0, +) -> None: + """Save model, optimizer, scheduler and training stats to file. + + Args: + params: + It is returned by :func:`get_params`. + model: + The training model. + model_avg: + The stored model averaged from the start of training. + optimizer: + The optimizer used in the training. + sampler: + The sampler for the training dataset. + scaler: + The scaler used for mix precision training. + """ + if rank != 0: + return + filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt" + save_checkpoint_impl( + filename=filename, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=sampler, + scaler=scaler, + rank=rank, + ) + + if params.best_train_epoch == params.cur_epoch: + best_train_filename = params.exp_dir / "best-train-loss.pt" + copyfile(src=filename, dst=best_train_filename) + + if params.best_valid_epoch == params.cur_epoch: + best_valid_filename = params.exp_dir / "best-valid-loss.pt" + copyfile(src=filename, dst=best_valid_filename) + + +def is_libri(c: Cut) -> bool: + """Return True if this cut is from the LibriSpeech dataset. + + Note: + During data preparation, we set the custom field in + the supervision segment of GigaSpeech to dict(origin='giga') + See ../local/preprocess_gigaspeech.py. + """ + return c.supervisions[0].custom is None + + +def compute_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + batch: dict, + is_training: bool, +) -> Tuple[Tensor, MetricsTracker]: + """ + Compute transducer loss given the model and its inputs. + + Args: + params: + Parameters for training. See :func:`get_params`. + model: + The model for training. It is an instance of Zipformer in our case. + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + is_training: + True for training. False for validation. When it is True, this + function enables autograd during computation; when it is False, it + disables autograd. + warmup: a floating point value which increases throughout training; + values >= 1.0 are fully warmed up and have all modules present. + """ + device = model.device if isinstance(model, DDP) else next(model.parameters()).device + feature = batch["inputs"] + # at entry, feature is (N, T, C) + assert feature.ndim == 3 + feature = feature.to(device) + + supervisions = batch["supervisions"] + feature_lens = supervisions["num_frames"].to(device) + + batch_idx_train = params.batch_idx_train + warm_step = params.warm_step + + texts = batch["supervisions"]["text"] + y = sp.encode(texts, out_type=int) + y = k2.RaggedTensor(y).to(device) + + with torch.set_grad_enabled(is_training): + simple_loss, pruned_loss = model( + x=feature, + x_lens=feature_lens, + y=y, + prune_range=params.prune_range, + am_scale=params.am_scale, + lm_scale=params.lm_scale, + ) + + s = params.simple_loss_scale + # take down the scale on the simple loss from 1.0 at the start + # to params.simple_loss scale by warm_step. + simple_loss_scale = ( + s + if batch_idx_train >= warm_step + else 1.0 - (batch_idx_train / warm_step) * (1.0 - s) + ) + pruned_loss_scale = ( + 1.0 + if batch_idx_train >= warm_step + else 0.1 + 0.9 * (batch_idx_train / warm_step) + ) + + loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss + + assert loss.requires_grad == is_training + + info = MetricsTracker() + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + info["frames"] = (feature_lens // params.subsampling_factor).sum().item() + + # Note: We use reduction=sum while computing the loss. + info["loss"] = loss.detach().cpu().item() + info["simple_loss"] = simple_loss.detach().cpu().item() + info["pruned_loss"] = pruned_loss.detach().cpu().item() + + return loss, info + + +def compute_validation_loss( + params: AttributeDict, + model: Union[nn.Module, DDP], + sp: spm.SentencePieceProcessor, + valid_dl: torch.utils.data.DataLoader, + world_size: int = 1, +) -> MetricsTracker: + """Run the validation process.""" + model.eval() + + tot_loss = MetricsTracker() + + for batch_idx, batch in enumerate(valid_dl): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=False, + ) + assert loss.requires_grad is False + tot_loss = tot_loss + loss_info + + if world_size > 1: + tot_loss.reduce(loss.device) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + if loss_value < params.best_valid_loss: + params.best_valid_epoch = params.cur_epoch + params.best_valid_loss = loss_value + + return tot_loss + + +def train_one_epoch( + params: AttributeDict, + model: Union[nn.Module, DDP], + optimizer: torch.optim.Optimizer, + scheduler: LRSchedulerType, + sp: spm.SentencePieceProcessor, + train_dl: torch.utils.data.DataLoader, + giga_train_dl: torch.utils.data.DataLoader, + valid_dl: torch.utils.data.DataLoader, + rng: random.Random, + scaler: GradScaler, + model_avg: Optional[nn.Module] = None, + tb_writer: Optional[SummaryWriter] = None, + world_size: int = 1, + rank: int = 0, +) -> None: + """Train the model for one epoch. + + The training loss from the mean of all frames is saved in + `params.train_loss`. It runs the validation process every + `params.valid_interval` batches. + + Args: + params: + It is returned by :func:`get_params`. + model: + The model for training. + optimizer: + The optimizer we are using. + scheduler: + The learning rate scheduler, we call step() every step. + train_dl: + Dataloader for the training dataset. + valid_dl: + Dataloader for the validation dataset. + scaler: + The scaler used for mix precision training. + model_avg: + The stored model averaged from the start of training. + tb_writer: + Writer to write log messages to tensorboard. + world_size: + Number of nodes in DDP training. If it is 1, DDP is disabled. + rank: + The rank of the node in DDP training. If no DDP is used, it should + be set to 0. + """ + model.train() + + libri_tot_loss = MetricsTracker() + giga_tot_loss = MetricsTracker() + tot_loss = MetricsTracker() + + # index 0: for LibriSpeech + # index 1: for GigaSpeech + # This sets the probabilities for choosing which datasets + dl_weights = [1 - params.giga_prob, params.giga_prob] + iter_libri = iter(train_dl) + iter_giga = iter(giga_train_dl) + + batch_idx = 0 + + while True: + idx = rng.choices((0, 1), weights=dl_weights, k=1)[0] + dl = iter_libri if idx == 0 else iter_giga + + try: + batch = next(dl) + except StopIteration: + name = "libri" if idx == 0 else "giga" + logging.info(f"{name} reaches end of dataloader") + break + + batch_idx += 1 + + params.batch_idx_train += 1 + batch_size = len(batch["supervisions"]["text"]) + + libri = is_libri(batch["supervisions"]["cut"][0]) + + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, loss_info = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + # summary stats + tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info + + if libri: + libri_tot_loss = ( + libri_tot_loss * (1 - 1 / params.reset_interval) + ) + loss_info + prefix = "libri" # for logging only + else: + giga_tot_loss = ( + giga_tot_loss * (1 - 1 / params.reset_interval) + ) + loss_info + prefix = "giga" + + # NOTE: We use reduction==sum and loss is computed over utterances + # in the batch and there is no normalization to it so far. + scaler.scale(loss).backward() + set_batch_count(model, params.batch_idx_train) + scheduler.step_batch(params.batch_idx_train) + + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + except: # noqa + display_and_save_batch(batch, params=params, sp=sp) + raise + + if params.print_diagnostics and batch_idx == 5: + return + + if ( + rank == 0 + and params.batch_idx_train > 0 + and params.batch_idx_train % params.average_period == 0 + ): + update_averaged_model( + params=params, + model_cur=model, + model_avg=model_avg, + ) + + if ( + params.batch_idx_train > 0 + and params.batch_idx_train % params.save_every_n == 0 + ): + params.cur_batch_idx = batch_idx + save_checkpoint_with_global_batch_idx( + out_dir=params.exp_dir, + global_batch_idx=params.batch_idx_train, + model=model, + model_avg=model_avg, + params=params, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + del params.cur_batch_idx + remove_checkpoints( + out_dir=params.exp_dir, + topk=params.keep_last_k, + rank=rank, + ) + + if batch_idx % 100 == 0 and params.use_fp16: + # If the grad scale was less than 1, try increasing it. The _growth_interval + # of the grad scaler is configurable, but we can't configure it to have different + # behavior depending on the current grad scale. + cur_grad_scale = scaler._scale.item() + if cur_grad_scale < 1.0 or (cur_grad_scale < 8.0 and batch_idx % 400 == 0): + scaler.update(cur_grad_scale * 2.0) + if cur_grad_scale < 0.01: + logging.warning(f"Grad scale is small: {cur_grad_scale}") + if cur_grad_scale < 1.0e-05: + raise RuntimeError( + f"grad_scale is too small, exiting: {cur_grad_scale}" + ) + + if batch_idx % params.log_interval == 0: + cur_lr = scheduler.get_last_lr()[0] + cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0 + + logging.info( + f"Epoch {params.cur_epoch}, " + f"batch {batch_idx}, {prefix}_loss[{loss_info}], " + f"tot_loss[{tot_loss}], " + f"libri_tot_loss[{libri_tot_loss}], " + f"giga_tot_loss[{giga_tot_loss}], " + f"batch size: {batch_size}, " + f"lr: {cur_lr:.2e}, " + + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "") + ) + + if tb_writer is not None: + tb_writer.add_scalar( + "train/learning_rate", cur_lr, params.batch_idx_train + ) + + loss_info.write_summary( + tb_writer, "train/current_", params.batch_idx_train + ) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train) + libri_tot_loss.write_summary( + tb_writer, "train/libri_tot_", params.batch_idx_train + ) + giga_tot_loss.write_summary( + tb_writer, "train/giga_tot_", params.batch_idx_train + ) + if params.use_fp16: + tb_writer.add_scalar( + "train/grad_scale", + cur_grad_scale, + params.batch_idx_train, + ) + + if batch_idx % params.valid_interval == 0 and not params.print_diagnostics: + logging.info("Computing validation loss") + valid_info = compute_validation_loss( + params=params, + model=model, + sp=sp, + valid_dl=valid_dl, + world_size=world_size, + ) + model.train() + logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}") + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + if tb_writer is not None: + valid_info.write_summary( + tb_writer, "train/valid_", params.batch_idx_train + ) + + loss_value = tot_loss["loss"] / tot_loss["frames"] + params.train_loss = loss_value + if params.train_loss < params.best_train_loss: + params.best_train_epoch = params.cur_epoch + params.best_train_loss = params.train_loss + + +def filter_short_and_long_utterances( + cuts: CutSet, sp: spm.SentencePieceProcessor +) -> CutSet: + def remove_short_and_long_utt(c: Cut): + # Keep only utterances with duration between 1 second and 20 seconds + # + # Caution: There is a reason to select 20.0 here. Please see + # ../local/display_manifest_statistics.py + # + # You should use ../local/display_manifest_statistics.py to get + # an utterance duration distribution for your dataset to select + # the threshold + if c.duration < 1.0 or c.duration > 20.0: + return False + + # In pruned RNN-T, we require that T >= S + # where T is the number of feature frames after subsampling + # and S is the number of tokens in the utterance + + # In ./zipformer.py, the conv module uses the following expression + # for subsampling + T = ((c.num_frames - 7) // 2 + 1) // 2 + tokens = sp.encode(c.supervisions[0].text, out_type=str) + + if T < len(tokens): + return False + + return True + + cuts = cuts.filter(remove_short_and_long_utt) + + return cuts + + +def run(rank, world_size, args): + """ + Args: + rank: + It is a value between 0 and `world_size-1`, which is + passed automatically by `mp.spawn()` in :func:`main`. + The node with rank 0 is responsible for saving checkpoint. + world_size: + Number of GPUs for DDP training. + args: + The return value of get_parser().parse_args() + """ + params = get_params() + params.update(vars(args)) + if params.full_libri is False: + params.valid_interval = 1600 + + fix_random_seed(params.seed) + rng = random.Random(params.seed) + if world_size > 1: + setup_dist(rank, world_size, params.master_port) + + setup_logger(f"{params.exp_dir}/log/log-train") + logging.info("Training started") + + if args.tensorboard and rank == 0: + tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard") + else: + tb_writer = None + + device = torch.device("cpu") + if torch.cuda.is_available(): + device = torch.device("cuda", rank) + logging.info(f"Device: {device}") + + sp = spm.SentencePieceProcessor() + sp.load(params.bpe_model) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + params.vocab_size = sp.get_piece_size() + + logging.info(params) + + logging.info("About to create model") + model = get_transducer_model(params) + + num_param = sum([p.numel() for p in model.parameters()]) + logging.info(f"Number of model parameters: {num_param}") + + assert params.save_every_n >= params.average_period + model_avg: Optional[nn.Module] = None + if rank == 0: + # model_avg is only used with rank 0 + model_avg = copy.deepcopy(model).to(torch.float64) + + assert params.start_epoch > 0, params.start_epoch + checkpoints = load_checkpoint_if_available( + params=params, model=model, model_avg=model_avg + ) + + model.to(device) + if world_size > 1: + logging.info("Using DDP") + model = DDP(model, device_ids=[rank], find_unused_parameters=True) + + parameters_names = [] + parameters_names.append( + [name_param_pair[0] for name_param_pair in model.named_parameters()] + ) + optimizer = ScaledAdam( + model.parameters(), + lr=params.base_lr, + clipping_scale=2.0, + parameters_names=parameters_names, + ) + + scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs) + + if checkpoints and "optimizer" in checkpoints: + logging.info("Loading optimizer state dict") + optimizer.load_state_dict(checkpoints["optimizer"]) + + if ( + checkpoints + and "scheduler" in checkpoints + and checkpoints["scheduler"] is not None + ): + logging.info("Loading scheduler state dict") + scheduler.load_state_dict(checkpoints["scheduler"]) + + if params.print_diagnostics: + opts = diagnostics.TensorDiagnosticOptions( + 2**22 + ) # allow 4 megabytes per sub-module + diagnostic = diagnostics.attach_diagnostics(model, opts) + + if params.inf_check: + register_inf_check_hooks(model) + + librispeech = LibriSpeech(manifest_dir=args.manifest_dir) + + train_cuts = librispeech.train_clean_100_cuts() + if params.full_libri: + train_cuts += librispeech.train_clean_360_cuts() + train_cuts += librispeech.train_other_500_cuts() + + train_cuts = filter_short_and_long_utterances(train_cuts, sp) + + gigaspeech = GigaSpeech(manifest_dir=args.manifest_dir) + # XL 10k hours + # L 2.5k hours + # M 1k hours + # S 250 hours + # XS 10 hours + # DEV 12 hours + # Test 40 hours + if params.full_libri: + logging.info("Using the XL subset of GigaSpeech (10k hours)") + train_giga_cuts = gigaspeech.train_XL_cuts() + else: + logging.info("Using the S subset of GigaSpeech (250 hours)") + train_giga_cuts = gigaspeech.train_S_cuts() + + train_giga_cuts = filter_short_and_long_utterances(train_giga_cuts, sp) + train_giga_cuts = train_giga_cuts.repeat(times=None) + + if args.enable_musan: + cuts_musan = load_manifest(Path(args.manifest_dir) / "musan_cuts.jsonl.gz") + else: + cuts_musan = None + + asr_datamodule = AsrDataModule(args) + + train_dl = asr_datamodule.train_dataloaders( + train_cuts, + on_the_fly_feats=False, + cuts_musan=cuts_musan, + ) + + giga_train_dl = asr_datamodule.train_dataloaders( + train_giga_cuts, + on_the_fly_feats=False, + cuts_musan=cuts_musan, + ) + + if params.start_batch > 0 and checkpoints and "sampler" in checkpoints: + # We only load the sampler's state dict when it loads a checkpoint + # saved in the middle of an epoch + sampler_state_dict = checkpoints["sampler"] + else: + sampler_state_dict = None + + valid_cuts = librispeech.dev_clean_cuts() + valid_cuts += librispeech.dev_other_cuts() + valid_dl = asr_datamodule.valid_dataloaders(valid_cuts) + + # if not params.print_diagnostics: + # scan_pessimistic_batches_for_oom( + # model=model, + # train_dl=train_dl, + # optimizer=optimizer, + # sp=sp, + # params=params, + # ) + + scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) + if checkpoints and "grad_scaler" in checkpoints: + logging.info("Loading grad scaler state dict") + scaler.load_state_dict(checkpoints["grad_scaler"]) + + for epoch in range(params.start_epoch, params.num_epochs + 1): + scheduler.step_epoch(epoch - 1) + fix_random_seed(params.seed + epoch - 1) + train_dl.sampler.set_epoch(epoch - 1) + + if tb_writer is not None: + tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train) + + params.cur_epoch = epoch + + train_one_epoch( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sp=sp, + train_dl=train_dl, + giga_train_dl=giga_train_dl, + valid_dl=valid_dl, + rng=rng, + scaler=scaler, + tb_writer=tb_writer, + world_size=world_size, + rank=rank, + ) + + if params.print_diagnostics: + diagnostic.print_diagnostics() + break + + save_checkpoint( + params=params, + model=model, + model_avg=model_avg, + optimizer=optimizer, + scheduler=scheduler, + sampler=train_dl.sampler, + scaler=scaler, + rank=rank, + ) + + logging.info("Done!") + + if world_size > 1: + torch.distributed.barrier() + cleanup_dist() + + +def display_and_save_batch( + batch: dict, + params: AttributeDict, + sp: spm.SentencePieceProcessor, +) -> None: + """Display the batch statistics and save the batch into disk. + + Args: + batch: + A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()` + for the content in it. + params: + Parameters for training. See :func:`get_params`. + sp: + The BPE model. + """ + from lhotse.utils import uuid4 + + filename = f"{params.exp_dir}/batch-{uuid4()}.pt" + logging.info(f"Saving batch to {filename}") + torch.save(batch, filename) + + supervisions = batch["supervisions"] + features = batch["inputs"] + + logging.info(f"features shape: {features.shape}") + + y = sp.encode(supervisions["text"], out_type=int) + num_tokens = sum(len(i) for i in y) + logging.info(f"num tokens: {num_tokens}") + + +def scan_pessimistic_batches_for_oom( + model: Union[nn.Module, DDP], + train_dl: torch.utils.data.DataLoader, + optimizer: torch.optim.Optimizer, + sp: spm.SentencePieceProcessor, + params: AttributeDict, +): + from lhotse.dataset import find_pessimistic_batches + + logging.info( + "Sanity check -- see if any of the batches in epoch 1 would cause OOM." + ) + batches, crit_values = find_pessimistic_batches(train_dl.sampler) + for criterion, cuts in batches.items(): + batch = train_dl.dataset[cuts] + try: + with torch.cuda.amp.autocast(enabled=params.use_fp16): + loss, _ = compute_loss( + params=params, + model=model, + sp=sp, + batch=batch, + is_training=True, + ) + loss.backward() + optimizer.zero_grad() + except Exception as e: + if "CUDA out of memory" in str(e): + logging.error( + "Your GPU ran out of memory with the current " + "max_duration setting. We recommend decreasing " + "max_duration and trying again.\n" + f"Failing criterion: {criterion} " + f"(={crit_values[criterion]}) ..." + ) + display_and_save_batch(batch, params=params, sp=sp) + raise + logging.info( + f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB" + ) + + +def main(): + parser = get_parser() + AsrDataModule.add_arguments(parser) + args = parser.parse_args() + args.exp_dir = Path(args.exp_dir) + + assert 0 <= args.giga_prob < 1, args.giga_prob + + world_size = args.world_size + assert world_size >= 1 + if world_size > 1: + mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True) + else: + run(rank=0, world_size=1, args=args) + + +torch.set_num_threads(1) +torch.set_num_interop_threads(1) + +if __name__ == "__main__": + main() diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train2.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train2.py new file mode 120000 index 0000000000..3c3280b688 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train2.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/train2.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer.py new file mode 120000 index 0000000000..be9e75bfa8 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/zipformer.py \ No newline at end of file diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer2.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer2.py new file mode 120000 index 0000000000..d3625f4789 --- /dev/null +++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/zipformer2.py @@ -0,0 +1 @@ +../pruned_transducer_stateless7_streaming/zipformer2.py \ No newline at end of file