🥇 Add hqq optimization algorithm

README.md

@@ -17,7 +17,7 @@
 ## 🛠️ Installation
 
 ```bash
-pip install whisperplus
+pip install whisperplus git+https://github.com/huggingface/transformers
 pip install flash-attn --no-build-isolation
 ```
 
@@ -33,12 +33,35 @@ To use the whisperplus library, follow the steps below for different tasks:
 
 ```python
 from whisperplus import SpeechToTextPipeline, download_and_convert_to_mp3
+from transformers import BitsAndBytesConfig, HqqConfig
 
-url = "https://www.youtube.com/watch?v=di3rHkEZuUw"
 
+url = "https://www.youtube.com/watch?v=di3rHkEZuUw"
 audio_path = download_and_convert_to_mp3(url)
-pipeline = SpeechToTextPipeline(model_id="openai/whisper-large-v3")
-transcript = pipeline(audio_path, "openai/whisper-large-v3", "english")
+
+quant_config  = HqqConfig(
+    nbits=1,
+    group_size=64,
+    quant_zero=False,
+    quant_scale=False, axis=0) #axis=0 is used by default
+
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+)
+
+pipeline = SpeechToTextPipeline(model_id=distil-whisper/distil-large-v3, quant_config=quant_config) # or bnb_config
+transcript = pipeline(
+    audio_path: str = "test.mp3",
+    chunk_length_s: int = 30,
+    stride_length_s: int = 5,
+    max_new_tokens: int = 128,
+    batch_size: int = 100,
+    language: str = "english",
+)
 
 print(transcript)
 ```

requirements.txt

@@ -1,22 +1,21 @@
 torch>=2.0.0
 torchvision>=0.15.0
 torchaudio>=2.0.0
-gradio==4.14.0
-langchain==0.1.0
-moviepy==1.0.3
-numpy==1.24.1
-pyannote.audio==3.1.0
-pyannote.core==5.0.0
-pyannote.database==5.0.1
-pyannote.metrics==3.2.1
-pyannote.pipeline==3.0.1
-pytube==15.0.0
-Requests==2.31.0
-transformers==4.35.2
+gradio>=4.14.0
+langchain>=0.1.0
+moviepy>=1.0.3
+numpy>=1.24.1
+pyannote.audio>=3.1.0
+pyannote.core>=5.0.0
+pyannote.database>=5.0.1
+pyannote.metrics>=3.2.1
+pyannote.pipeline>=3.0.1
+pytube>=15.0.0
+Requests>=2.31.0
 sentence-transformers
 ctransformers
 accelerate
 pre-commit==3.4.0
-autollm==0.1.9
-speechbrain==0.5.16
+autollm>=0.1.9
+speechbrain>=0.5.16
 bitsandbytes

whisperplus/__init__.py

@@ -7,6 +7,6 @@
 from whisperplus.utils.download_utils import download_and_convert_to_mp3
 from whisperplus.utils.text_utils import format_speech_to_dialogue
 
-__version__ = '0.2.7'
+__version__ = '0.2.7.2.dev1'
 __author__ = 'kadirnar'
 __license__ = 'Apache License 2.0'

whisperplus/pipelines/whisper.py

@@ -1,7 +1,6 @@
 import logging
 
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, BitsAndBytesConfig, pipeline
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -18,24 +17,17 @@ def __init__(self, model_id: str = "openai/whisper-large-v3"):
         else:
             logging.info("Model already loaded.")
 
-    def load_model(self, model_id: str = "openai/whisper-large-v3"):
+    def load_model(self, model_id: str = "openai/whisper-large-v3", quant_config=None):
         """
         Loads the pre-trained speech recognition model and moves it to the specified device.
 
         Args:
             model_id (str): Identifier of the pre-trained model to be loaded.
         """
         logging.info("Loading model...")
-
-        bnb_config = BitsAndBytesConfig(
-            load_in_4bit=True,
-            bnb_4bit_quant_type="nf4",
-            bnb_4bit_compute_dtype=torch.bfloat16,
-            bnb_4bit_use_double_quant=True,
-        )
         model = AutoModelForSpeechSeq2Seq.from_pretrained(
             model_id,
-            quantization_config=bnb_config,
+            quantization_config=quant_config,
             low_cpu_mem_usage=True,
             use_safetensors=True,
             attn_implementation="flash_attention_2",