Merge pull request #82 from hcljsq/main

Add the `large-v3` model

Audio-Transcription-Chrome/popup.html

@@ -140,6 +140,7 @@
       <option value="small" selected>Small</option>
       <option value="medium">Medium</option>
       <option value="large-v2">Large-v2</option>
+      <option value="large-v3">Large-v3</option>
     </select>
   </div>
 </body>

Audio-Transcription-Firefox/popup.html

@@ -142,6 +142,7 @@
       <option value="small" selected>Small</option>
       <option value="medium">Medium</option>
       <option value="large-v2">Large-v2</option>
+      <option value="large-v3">Large-v3</option>
     </select>
   </div>
 </body>

requirements/server.txt

@@ -1,5 +1,5 @@
 PyAudio
-faster-whisper==0.9.0
+faster-whisper==0.10.0
 --extra-index-url https://download.pytorch.org/whl/cu111
 torch==1.10.1
 torchaudio==0.10.1

setup.py

@@ -41,7 +41,7 @@
       ),
       install_requires=[
         "PyAudio",
-        "faster-whisper==0.9.0",
+        "faster-whisper==0.10.0",
         "torch",
         "torchaudio",
         "websockets",

whisper_live/server.py

@@ -211,7 +211,7 @@ def __init__(
         self.data = b""
         self.frames = b""
         self.model_sizes = [
-            "tiny", "base", "small", "medium", "large-v2"
+            "tiny", "base", "small", "medium", "large-v2", "large-v3"
         ]
         self.multilingual = multilingual
         self.model_size = self.get_model_size(model_size)
@@ -277,7 +277,7 @@ def get_model_size(self, model_size):
             )
             return None
 
-        if model_size == "large-v2":
+        if model_size in ["large-v2", "large-v3"]:
             self.multilingual = True
             return model_size
 

whisper_live/transcriber.py

@@ -4,6 +4,8 @@
 import logging
 import os
 import zlib
+import json
+from inspect import signature
 
 from typing import BinaryIO, Iterable, List, NamedTuple, Optional, Tuple, Union
 
@@ -94,7 +96,7 @@ def __init__(
 
         Args:
           model_size_or_path: Size of the model to use (tiny, tiny.en, base, base.en,
-            small, small.en, medium, medium.en, large-v1, large-v2, or large), a path to a converted
+            small, small.en, medium, medium.en, large-v1, large-v2, large-v3, or large), a path to a converted
             model directory, or a CTranslate2-converted Whisper model ID from the Hugging Face Hub.
             When a size or a model ID is configured, the converted model is downloaded
             from the Hugging Face Hub.
@@ -144,7 +146,8 @@ def __init__(
                 "openai/whisper-tiny" + ("" if self.model.is_multilingual else ".en")
             )
 
-        self.feature_extractor = FeatureExtractor()
+        self.feat_kwargs = self._get_feature_kwargs(model_path)
+        self.feature_extractor = FeatureExtractor(**self.feat_kwargs)
         self.num_samples_per_token = self.feature_extractor.hop_length * 2
         self.frames_per_second = (
             self.feature_extractor.sampling_rate // self.feature_extractor.hop_length
@@ -161,6 +164,22 @@ def supported_languages(self) -> List[str]:
         """The languages supported by the model."""
         return list(_LANGUAGE_CODES) if self.model.is_multilingual else ["en"]
 
+    def _get_feature_kwargs(self, model_path) -> dict:
+        preprocessor_config_file = os.path.join(model_path, "preprocessor_config.json")
+        config = {}
+        if os.path.isfile(preprocessor_config_file):
+            try:
+                with open(preprocessor_config_file, "r", encoding="utf-8") as json_file:
+                    config = json.load(json_file)
+                valid_keys = signature(FeatureExtractor.__init__).parameters.keys()
+                config = {k: v for k, v in config.items() if k in valid_keys}
+            except json.JSONDecodeError as e:
+                self.logger.warning(
+                    "Could not load preprocessor_config.json: %s", str(e)
+                )
+
+        return config
+
     def transcribe(
         self,
         audio: Union[str, BinaryIO, np.ndarray],
@@ -914,7 +933,7 @@ def find_alignment(
                 words, word_tokens, start_times, end_times, word_probabilities
             )
         ]
-    
+
     def destroy(self):
         del self.model