Merge branch 'bugfix_whisper' into develop

controller.py

@@ -806,6 +806,7 @@ def callbackSetUserWhisperFeature(value):
             config.SELECTED_TRANSCRIPTION_ENGINE = "Google"
     else:
         view.closeWhisperWeightTypeWidget()
+        config.SELECTED_TRANSCRIPTION_ENGINE = "Google"
     view.showRestartButtonIfRequired()
 
 def callbackSetWhisperWeightType(value):

model.py

@@ -1,3 +1,4 @@
+import gc
 import tempfile
 from zipfile import ZipFile
 from subprocess import Popen
@@ -336,22 +337,29 @@ class Model:
         )
         # self.mic_audio_recorder.recordIntoQueue(mic_audio_queue, mic_energy_queue)
         self.mic_audio_recorder.recordIntoQueue(mic_audio_queue, None)
-        mic_transcriber = AudioTranscriber(
+        self.mic_transcriber = AudioTranscriber(
             speaker=False,
             source=self.mic_audio_recorder.source,
             phrase_timeout=phase_timeout,
             max_phrases=config.INPUT_MIC_MAX_PHRASES,
+            transcription_engine=config.SELECTED_TRANSCRIPTION_ENGINE,
             root=config.PATH_LOCAL,
             whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
         )
         def sendMicTranscript():
-            mic_transcriber.transcribeAudioQueue(mic_audio_queue, config.SOURCE_LANGUAGE, config.SOURCE_COUNTRY, config.SELECTED_TRANSCRIPTION_ENGINE)
-            message = mic_transcriber.getTranscript()
+            self.mic_transcriber.transcribeAudioQueue(mic_audio_queue, config.SOURCE_LANGUAGE, config.SOURCE_COUNTRY)
+            message = self.mic_transcriber.getTranscript()
             try:
                 fnc(message)
             except Exception:
                 pass
 
+        def endMicTranscript():
+            mic_audio_queue.queue.clear()
+            # mic_energy_queue.queue.clear()
+            del self.mic_transcriber
+            gc.collect()
+
         # def sendMicEnergy():
         #     if mic_energy_queue.empty() is False:
         #         energy = mic_energy_queue.get()
@@ -362,7 +370,7 @@ class Model:
         #             pass
         #     sleep(0.01)
 
-        self.mic_print_transcript = threadFnc(sendMicTranscript)
+        self.mic_print_transcript = threadFnc(sendMicTranscript, end_fnc=endMicTranscript)
         self.mic_print_transcript.daemon = True
         self.mic_print_transcript.start()
 
@@ -438,22 +446,29 @@ class Model:
         )
         # self.speaker_audio_recorder.recordIntoQueue(speaker_audio_queue, speaker_energy_queue)
         self.speaker_audio_recorder.recordIntoQueue(speaker_audio_queue ,None)
-        speaker_transcriber = AudioTranscriber(
+        self.speaker_transcriber = AudioTranscriber(
             speaker=True,
             source=self.speaker_audio_recorder.source,
             phrase_timeout=phase_timeout,
             max_phrases=config.INPUT_SPEAKER_MAX_PHRASES,
+            transcription_engine=config.SELECTED_TRANSCRIPTION_ENGINE,
             root=config.PATH_LOCAL,
             whisper_weight_type=config.WHISPER_WEIGHT_TYPE,
         )
         def sendSpeakerTranscript():
-            speaker_transcriber.transcribeAudioQueue(speaker_audio_queue, config.TARGET_LANGUAGE, config.TARGET_COUNTRY, config.SELECTED_TRANSCRIPTION_ENGINE)
-            message = speaker_transcriber.getTranscript()
+            self.speaker_transcriber.transcribeAudioQueue(speaker_audio_queue, config.TARGET_LANGUAGE, config.TARGET_COUNTRY)
+            message = self.speaker_transcriber.getTranscript()
             try:
                 fnc(message)
             except Exception:
                 pass
 
+        def endSpeakerTranscript():
+            speaker_audio_queue.queue.clear()
+            # speaker_energy_queue.queue.clear()
+            del self.speaker_transcriber
+            gc.collect()
+
         # def sendSpeakerEnergy():
         #     if speaker_energy_queue.empty() is False:
         #         energy = speaker_energy_queue.get()
@@ -464,7 +479,7 @@ class Model:
         #             pass
         #     sleep(0.01)
 
-        self.speaker_print_transcript = threadFnc(sendSpeakerTranscript)
+        self.speaker_print_transcript = threadFnc(sendSpeakerTranscript, end_fnc=endSpeakerTranscript)
         self.speaker_print_transcript.daemon = True
         self.speaker_print_transcript.start()
 

models/transcription/transcription_transcriber.py

@@ -14,13 +14,15 @@ PHRASE_TIMEOUT = 3
 MAX_PHRASES = 10
 
 class AudioTranscriber:
-    def __init__(self, speaker, source, phrase_timeout, max_phrases, root=None, whisper_weight_type=None, ):
+    def __init__(self, speaker, source, phrase_timeout, max_phrases, transcription_engine, root=None, whisper_weight_type=None):
         self.speaker = speaker
         self.phrase_timeout = phrase_timeout
         self.max_phrases = max_phrases
         self.transcript_data = []
         self.transcript_changed_event = Event()
         self.audio_recognizer = Recognizer()
+        self.transcription_engine = "Google"
+        self.whisper_model = None
         self.audio_sources = {
                 "sample_rate": source.SAMPLE_RATE,
                 "sample_width": source.SAMPLE_WIDTH,
@@ -30,26 +32,21 @@ class AudioTranscriber:
                 "new_phrase": True,
                 "process_data_func": self.processSpeakerData if speaker else self.processSpeakerData
         }
-        if whisper_weight_type is not None and root is not None and checkWhisperWeight(root, whisper_weight_type) is True:
-            self.whisper_model = getWhisperModel(root, whisper_weight_type)
-        else:
-            self.whisper_model = None
 
-    def transcribeAudioQueue(self, audio_queue, language, country, transcription_engine):
+        if transcription_engine == "Whisper" and checkWhisperWeight(root, whisper_weight_type) is True:
+            self.whisper_model = getWhisperModel(root, whisper_weight_type)
+            self.transcription_engine = "Whisper"
+
+    def transcribeAudioQueue(self, audio_queue, language, country):
         audio, time_spoken = audio_queue.get()
         self.updateLastSampleAndPhraseStatus(audio, time_spoken)
 
         text = ''
         try:
-            # Whisperが使用できない場合はGoogle Speech-to-Textを使用する
-            if transcription_engine == "Whisper":
-                if self.whisper_model is None:
-                    transcription_engine = "Google"
-
             audio_data = self.audio_sources["process_data_func"]()
-            match transcription_engine:
+            match self.transcription_engine:
                 case "Google":
-                    text = self.audio_recognizer.recognize_google(audio_data, language=transcription_lang[language][country][transcription_engine])
+                    text = self.audio_recognizer.recognize_google(audio_data, language=transcription_lang[language][country][self.transcription_engine])
                 case "Whisper":
                     audio_data = np.frombuffer(audio_data.get_raw_data(convert_rate=16000, convert_width=2), np.int16).flatten().astype(np.float32) / 32768.0
                     if isinstance(audio_data, torch.Tensor):
@@ -60,7 +57,7 @@ class AudioTranscriber:
                         temperature=0.0,
                         log_prob_threshold=-0.8,
                         no_speech_threshold=0.6,
-                        language=transcription_lang[language][country][transcription_engine],
+                        language=transcription_lang[language][country][self.transcription_engine],
                         word_timestamps=False,
                         without_timestamps=True,
                         task="transcribe",