initial commit

.gitignore

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+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info
+.pytest_cache
+.ipynb_checkpoints
+
+thumbs.db
+.DS_Store
+.idea
+

LICENSE

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+MIT License
+
+Copyright (c) 2022 OpenAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+include whisper/assets/*
+include whisper/assets/gpt2/*
+include whisper/assets/multilingual/*
+include whisper/normalizers/english.json

README.md

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+# Whisper
+
+[[Blog]](https://openai.com/blog/whisper)
+[[Paper]](https://cdn.openai.com/papers/whisper.pdf)
+[[Model card]](model-card.md)
+[[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb)
+
+Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
+
+
+## Approach
+
+![Approach](approach.png)
+
+A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets.
+
+
+## Setup
+
+We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies 
+
+    pip install git+https://github.com/openai/whisper.git 
+
+It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:
+
+```bash
+# on Ubuntu or Debian
+sudo apt update && sudo apt install ffmpeg
+
+# on MacOS using Homebrew (https://brew.sh/)
+brew install ffmpeg                         
+
+# on Windows using Chocolatey (https://chocolatey.org/)
+choco install ffmpeg
+```
+
+
+## Command-line usage
+
+The following command will transcribe speech in audio files
+
+    whisper audio.flac audio.mp3 audio.wav
+
+The default setting works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
+
+    whisper ~/japanese.wav --language Japanese
+
+Adding `--task translate` will translate the speech into English:
+
+    whisper ~/japanese.wav --language Japanese --task translate
+
+Run the following to view all available transcription options:
+
+    whisper --help
+
+See [tokenizer.py](whisper/tokenizer.py) for the list of all available languages.
+
+
+## Python usage
+
+Transcription can also be performed within Python: 
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+result = model.transcribe("audio.mp3")
+print(result["text"])
+```
+
+Internally, the `transcribe()` method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.
+
+Below is an example usage of `whisper.detect_language()` and `whisper.decode()` which provide lower-level access to the model.
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+
+# load audio and pad/trim it to fit 30 seconds
+audio = whisper.load_audio("audio.mp3")
+audio = whisper.pad_or_trim(audio)
+
+# make log-Mel spectrogram and move to the same device as the model
+mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+# detect the spoken language
+_, probs = model.detect_language(mel)
+print(f"Detected language: {max(probs, key=probs.get)}")
+
+# decode the audio
+options = whisper.DecodingOptions()
+result = whisper.decode(model, mel, options)
+
+# print the recognized text
+print(result.text)
+```
+
+## License
+
+The code and the model weights of Whisper are released under the MIT License. See `LICENSE` for further details.

data/README.md

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+This directory supplements the paper with more details on how we prepared the data for evaluation, to help replicate our experiments. 
+
+## Short-form English-only datasets
+
+### LibriSpeech
+
+We used the test-clean and test-other splits from the [LibriSpeech ASR corpus](https://www.openslr.org/12).
+
+### TED-LIUM 3
+
+We used the test split of [TED-LIUM Release 3](https://www.openslr.org/51/), using the segmented manual transcripts included in the release.
+
+### Common Voice 5.1
+
+We downloaded the English subset of Common Voice Corpus 5.1 from [the official website](https://commonvoice.mozilla.org/en/datasets)
+
+### Artie
+
+We used the [Artie bias corpus](https://github.com/artie-inc/artie-bias-corpus). This is a subset of the Common Voice dataset.
+
+### CallHome & Switchboard
+
+We used the two corpora from [LDC2002S09](https://catalog.ldc.upenn.edu/LDC2002S09) and [LDC2002T43](https://catalog.ldc.upenn.edu/LDC2002T43) and followed the [eval2000_data_prep.sh](https://github.com/kaldi-asr/kaldi/blob/master/egs/fisher_swbd/s5/local/eval2000_data_prep.sh) script for preprocessing. The `wav.scp` files can be converted to WAV files with the following bash commands:
+
+```bash
+mkdir -p wav
+while read name cmd; do
+    echo $name
+    echo ${cmd/\|/} wav/$name.wav | bash
+done < wav.scp
+```
+
+
+### WSJ
+
+We used [LDC93S6B](https://catalog.ldc.upenn.edu/LDC93S6B) and [LDC94S13B](https://catalog.ldc.upenn.edu/LDC94S13B) and followed the [s5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/wsj/s5) to preprocess the dataset.
+
+### CORAAL
+
+We used the 231 interviews from [CORAAL (v. 2021.07)](https://oraal.uoregon.edu/coraal) and used the segmentations from [the FairSpeech project](https://github.com/stanford-policylab/asr-disparities/blob/master/input/CORAAL_transcripts.csv).
+
+### CHiME-6
+
+We downloaded the [CHiME-5 dataset](https://spandh.dcs.shef.ac.uk//chime_challenge/CHiME5/download.html) and followed the stage 0 of the [s5_track1 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1) to create the CHiME-6 dataset which fixes synchronization. We then used the binaural recordings (`*_P??.wav`) and the corresponding transcripts.
+
+### AMI-IHM, AMI-SDM1
+
+We preprocessed the [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) by following the stage 0 ad 2 of the [s5b recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5b).
+
+
+## Long-form English-only datasets
+
+### TED-LIUM 3
+
+To create a long-form transcription dataset from the [TED-LIUM3](https://www.openslr.org/51/) dataset, we sliced the audio between the beginning of the first labeled segment and the end of the last labeled segment of each talk, and we used the concatenated text as the label. Below are the timestamps used for slicing each of the 11 TED talks in the test split.   
+
+| Filename            | Begin time (s) | End time (s) |
+|---------------------|----------------|--------------|
+| DanBarber_2010      | 16.09          | 1116.24      |
+| JaneMcGonigal_2010  | 15.476         | 1187.61      |
+| BillGates_2010      | 15.861         | 1656.94      |
+| TomWujec_2010U      | 16.26          | 402.17       |
+| GaryFlake_2010      | 16.06          | 367.14       |
+| EricMead_2009P      | 18.434         | 536.44       |
+| MichaelSpecter_2010 | 16.11          | 979.312      |
+| DanielKahneman_2010 | 15.8           | 1199.44      |
+| AimeeMullins_2009P  | 17.82          | 1296.59      |
+| JamesCameron_2010   | 16.75          | 1010.65      |
+| RobertGupta_2010U   | 16.8           | 387.03       |
+
+### Meanwhile
+
+This dataset consists of 64 segments from The Late Show with Stephen Colbert. The YouTube video ID, start and end timestamps, and the labels can be found in [meanwhile.json](meanwhile.json). The labels are collected from the closed-caption data for each video and corrected with manual inspection.
+
+### Rev16
+
+We use a subset of 16 files from the 30 podcast episodes in [Rev.AI's Podcast Transcription Benchmark](https://www.rev.ai/blog/podcast-transcription-benchmark-part-1/), after finding that there are multiple cases where a significant portion of the audio and the labels did not match, mostly on the parts introducing the sponsors. We selected 16 episodes that do not have this error, whose "file number" are:
+
+    3 4 9 10 11 14 17 18 20 21 23 24 26 27 29 32
+
+### Kincaid46
+
+This dataset consists of 46 audio files and the corresponding transcripts compiled in the blog article [Which automatic transcription service is the most accurate - 2018](https://medium.com/descript/which-automatic-transcription-service-is-the-most-accurate-2018-2e859b23ed19) by Jason Kincaid. We used the 46 audio files and reference transcripts from the Airtable widget in the article.
+
+For the human transcription benchmark in the paper, we use a subset of 25 examples from this data, whose "Ref ID" are:
+
+    2 4 5 8 9 10 12 13 14 16 19 21 23 25 26 28 29 30 33 35 36 37 42 43 45
+
+### Earnings-21, Earnings-22
+
+For these datasets, we used the files available in [the speech-datasets repository](https://github.com/revdotcom/speech-datasets), as of their `202206` version.
+
+### CORAAL
+
+We used the 231 interviews from [CORAAL (v. 2021.07)](https://oraal.uoregon.edu/coraal) and used the full-length interview files and transcripts.
+
+
+## Multilingual datasets
+
+### Multilingual LibriSpeech
+
+We used the test splits from each language in [the Multilingual LibriSpeech (MLS) corpus](https://www.openslr.org/94/).
+
+### Fleurs
+
+We collected audio files and transcripts using the implementation available as [HuggingFace datasets](https://huggingface.co/datasets/google/fleurs/blob/main/fleurs.py). To use as a translation dataset, we matched the numerical utterance IDs to find the corresponding transcript in English.   
+
+### VoxPopuli
+
+We used the `get_asr_data.py` script from [the official repository](https://github.com/facebookresearch/voxpopuli) to collect the ASR data in 14 languages. 
+
+### Common Voice 9
+
+We downloaded the Common Voice Corpus 9 from [the official website](https://commonvoice.mozilla.org/en/datasets)
+
+### CoVOST 2
+
+We collected the `X into English` data collected using [the official repository](https://github.com/facebookresearch/covost).