[WIP/TEST] faster-whisper model weight のダウンロード/ベリファイ処理を実装

main.py

@@ -11,8 +11,14 @@ if __name__ == "__main__":
         from models.translation.utils import downloadCTranslate2Weight
         if config.USE_TRANSLATION_FEATURE is True:
             downloadCTranslate2Weight(config.PATH_LOCAL, config.CTRANSLATE2_WEIGHT_TYPE, splash.updateDownloadProgress)
-
         splash.toProgress(0)
+
+        # whisperのダウンロードの説明に変更する必要あり
+        if config.USE_RECOGNIZER_FEATURE is True:
+            from models.transcription.transcription_whisper import downloadWhisperWeight
+            downloadWhisperWeight(config.PATH_LOCAL, config.WHISPER_WEIGHT_TYPE, splash.updateDownloadProgress)
+        splash.toProgress(0)
+
         import controller
         controller.createMainWindow(splash)
         splash.destroySplash()

model.py

@@ -337,7 +337,7 @@ class Model:
             max_phrases=config.INPUT_MIC_MAX_PHRASES,
             whisper_enabled=config.USE_RECOGNIZER_FEATURE,
             whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
-            whisper_weight_path=os_path.join(config.PATH_LOCAL, "weight", "whisper"),
+            root=config.PATH_LOCAL,
         )
         def sendMicTranscript():
             mic_transcriber.transcribeAudioQueue(config.SELECTED_RECOGNIZER, mic_audio_queue, config.SOURCE_LANGUAGE, config.SOURCE_COUNTRY)
@@ -421,7 +421,7 @@ class Model:
             max_phrases=config.INPUT_SPEAKER_MAX_PHRASES,
             whisper_enabled=config.USE_RECOGNIZER_FEATURE,
             whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
-            whisper_weight_path=os_path.join(config.PATH_LOCAL, "weight", "whisper"),
+            root=config.PATH_LOCAL,
         )
         def sendSpeakerTranscript():
             speaker_transcriber.transcribeAudioQueue(speaker_audio_queue, config.TARGET_LANGUAGE, config.TARGET_COUNTRY)

models/transcription/transcription_transcriber.py

@@ -5,16 +5,16 @@ from speech_recognition import Recognizer, AudioData, AudioFile
 from datetime import timedelta
 from pyaudiowpatch import get_sample_size, paInt16
 from .transcription_languages import transcription_lang
+from .transcription_whisper import getWhisperModel
 
 import torch
 import numpy as np
-from faster_whisper import WhisperModel
 
 PHRASE_TIMEOUT = 3
 MAX_PHRASES = 10
 
 class AudioTranscriber:
-    def __init__(self, speaker, source, phrase_timeout, max_phrases, whisper_enabled, whisper_weight_type, whisper_weight_path):
+    def __init__(self, speaker, source, phrase_timeout, max_phrases, whisper_enabled, whisper_weight_type, root):
         self.speaker = speaker
         self.phrase_timeout = phrase_timeout
         self.max_phrases = max_phrases
@@ -31,14 +31,7 @@ class AudioTranscriber:
                 "process_data_func": self.processSpeakerData if speaker else self.processSpeakerData
         }
         if whisper_enabled is True:
-            self.whisper_model = WhisperModel(
+            self.whisper_model = getWhisperModel(root, whisper_weight_type)
-                model_size_or_path=whisper_weight_type,
-                device="cpu",
-                device_index=0,
-                compute_type="int8",
-                cpu_threads=4,
-                num_workers=1,
-                download_root=whisper_weight_path)
         else:
             self.whisper_model = None
 

models/transcription/transcription_utils.py

@@ -1,8 +1,4 @@
 from pyaudiowpatch import PyAudio, paWASAPI
-from faster_whisper.utils import download_model
-import logging
-logger = logging.getLogger('faster_whisper')
-logger.setLevel(logging.CRITICAL)
 
 def getInputDevices():
     devices = {}
@@ -48,38 +44,4 @@ def getDefaultOutputDevice():
                             if default_speakers["name"] in loopback["name"]:
                                 default_device = loopback
                                 return default_device
-    return {"name":"NoDevice"}
+    return {"name":"NoDevice"}
-
-def downloadWhisperWeight(weight_type, path):
-    result = False
-    try:
-        download_model(
-            weight_type,
-            cache_dir=path)
-        result = True
-    except Exception:
-        pass
-    return result
-
-def checkWhisperWeight(weight_type, path):
-    result = False
-    try:
-        result = download_model(
-            weight_type,
-            local_files_only=True,
-            cache_dir=path)
-        result = True
-    except Exception:
-        pass
-    return result
-
-if __name__ == "__main__":
-
-
-    downloadWhisperWeight("base", "./weight/whisper/")
-
-    from faster_whisper import WhisperModel
-    whisper_model = WhisperModel("base", device="cpu", device_index=0, compute_type="int8", cpu_threads=4, num_workers=1, download_root="./weight/whisper/")
-
-    print(checkWhisperWeight("base", "./weight/whisper/"))
-    print(checkWhisperWeight("tiny", "./weight/whisper/"))

models/transcription/transcription_whisper.py

@@ -0,0 +1,98 @@
+from os import path as os_path, makedirs as os_makedirs
+from requests import get as requests_get
+from typing import Callable
+import huggingface_hub
+from faster_whisper import WhisperModel
+import logging
+logger = logging.getLogger('faster_whisper')
+logger.setLevel(logging.CRITICAL)
+
+_MODELS = {
+    "tiny.en": "Systran/faster-whisper-tiny.en",
+    "tiny": "Systran/faster-whisper-tiny",
+    "base.en": "Systran/faster-whisper-base.en",
+    "base": "Systran/faster-whisper-base",
+    "small.en": "Systran/faster-whisper-small.en",
+    "small": "Systran/faster-whisper-small",
+    "medium.en": "Systran/faster-whisper-medium.en",
+    "medium": "Systran/faster-whisper-medium",
+    "large-v1": "Systran/faster-whisper-large-v1",
+    "large-v2": "Systran/faster-whisper-large-v2",
+    "large-v3": "Systran/faster-whisper-large-v3",
+    "large": "Systran/faster-whisper-large-v3",
+}
+
+_FILENAMES = [
+    "config.json",
+    "preprocessor_config.json",
+    "model.bin",
+    "tokenizer.json",
+    "vocabulary.txt",
+]
+
+def downloadFile(url, path, func=None):
+    try:
+        res = requests_get(url, stream=True)
+        res.raise_for_status()
+        file_size = int(res.headers.get('content-length', 0))
+        total_chunk = 0
+        with open(os_path.join(path), 'wb') as file:
+            for chunk in res.iter_content(chunk_size=1024*5):
+                file.write(chunk)
+                if isinstance(func, Callable):
+                    total_chunk += len(chunk)
+                    func(total_chunk/file_size)
+
+    except Exception as e:
+            print("error:downloadFile()", e)
+
+def checkWhisperWeight(path):
+    result = False
+    try:
+        WhisperModel(
+            path,
+            device="cpu",
+            device_index=0,
+            compute_type="int8",
+            cpu_threads=4,
+            num_workers=1,
+            local_files_only=True,
+        )
+        result = True
+    except Exception:
+        pass
+    return result
+
+def downloadWhisperWeight(root, weight_type, callbackFunc):
+    path = os_path.join(root, "weight", "whisper", weight_type)
+    os_makedirs(path, exist_ok=True)
+    if checkWhisperWeight(path) is True:
+        return
+
+    for filename in _FILENAMES:
+        print("Downloading", filename, "...")
+        file_path = os_path.join(path, filename)
+        url = huggingface_hub.hf_hub_url(_MODELS[weight_type], filename)
+        downloadFile(url, file_path, func=callbackFunc)
+
+def getWhisperModel(root, weight_type):
+    path = os_path.join(root, "weight", "whisper", weight_type)
+    return WhisperModel(
+        path,
+        device="cpu",
+        device_index=0,
+        compute_type="int8",
+        cpu_threads=4,
+        num_workers=1,
+        local_files_only=True,
+    )
+
+if __name__ == "__main__":
+    def callback(value):
+        print(value)
+
+    downloadWhisperWeight("./", "tiny", callback)
+    downloadWhisperWeight("./", "base", callback)
+    downloadWhisperWeight("./", "small", callback)
+    downloadWhisperWeight("./", "medium", callback)
+    downloadWhisperWeight("./", "large", callback)