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Fix for live transcription with Faster whisper (#787)

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Raivis Dejus 2024-06-08 19:13:14 +03:00 • committed by GitHub
commit c19e7acf08
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2 changed files with 23 additions and 2 deletions
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3
buzz/buzz.py
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@ -6,7 +6,7 @@ import platform
import sys
from typing import TextIO
from platformdirs import user_log_dir
from platformdirs import user_log_dir, user_cache_dir
from buzz.assets import APP_BASE_DIR
@ -59,6 +59,7 @@ def main():
logging.debug("app_dir: %s", APP_BASE_DIR)
logging.debug("log_dir: %s", log_dir)
logging.debug("cache_dir: %s", user_cache_dir("Buzz"))
app = Application(sys.argv)
parse_command_line(app)

22
buzz/transcriber/recording_transcriber.py
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@ -16,6 +16,7 @@ from buzz.transcriber.whisper_cpp import WhisperCpp, whisper_cpp_params
from buzz.transformers_whisper import TransformersWhisper
import whisper
import faster_whisper
class RecordingTranscriber(QObject):
@ -53,6 +54,8 @@ class RecordingTranscriber(QObject):
model = whisper.load_model(model_path)
elif self.transcription_options.model.model_type == ModelType.WHISPER_CPP:
model = WhisperCpp(model_path)
elif self.transcription_options.model.model_type == ModelType.FASTER_WHISPER:
model = faster_whisper.WhisperModel(model_path)
else: # ModelType.HUGGING_FACE
model = transformers_whisper.load_model(model_path)
@ -113,7 +116,24 @@ class RecordingTranscriber(QObject):
transcription_options=self.transcription_options
),
)
else:
elif (
self.transcription_options.model.model_type
== ModelType.FASTER_WHISPER
):
assert isinstance(model, faster_whisper.WhisperModel)
whisper_segments, info = model.transcribe(
audio=samples,
language=self.transcription_options.language
if self.transcription_options.language is not ""
else None,
task=self.transcription_options.task.value,
temperature=self.transcription_options.temperature,
initial_prompt=self.transcription_options.initial_prompt,
word_timestamps=self.transcription_options.word_level_timings,
)
result = {"text": " ".join([segment.text for segment in whisper_segments])}
else: # ModelType.HUGGING_FACE
assert isinstance(model, TransformersWhisper)
result = model.transcribe(
audio=samples,