voksik/faster-whisper-n-ionet-llm
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add parethese

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Changes:
        1. Add parethese at FasterWhisper class in app.py;
        2. Remove usless def __init__ in FasterWhisper.py
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swrneko
2025-09-08 21:25:52 +03:00
parent 00943f6525
commit 271846a8c5
2 changed files with 1 additions and 4 deletions
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2
app.py
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@@ -133,7 +133,7 @@ with gr.Blocks() as demo:
isPipelineEnabledCheckbox.change(updateButton, inputs=[isPipelineEnabledCheckbox], outputs=refineTextBtn) isPipelineEnabledCheckbox.change(updateButton, inputs=[isPipelineEnabledCheckbox], outputs=refineTextBtn)
saveFileCheckbox.change(updateTextbox, inputs=saveFileCheckbox, outputs=filename) saveFileCheckbox.change(updateTextbox, inputs=saveFileCheckbox, outputs=filename)
recognizeBtn.click(FasterWhisper.recognize, outputs=[recognizedText], inputs=[fastWhisperModel, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText]) recognizeBtn.click(FasterWhisper().recognize, outputs=[recognizedText], inputs=[fastWhisperModel, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText])
# Если пайплайн включен то тогда делаем автоматически # Если пайплайн включен то тогда делаем автоматически
# автоматический пайплайн # автоматический пайплайн

3
services/fasterWhisper.py
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@@ -1,9 +1,6 @@
from faster_whisper import WhisperModel from faster_whisper import WhisperModel
class FasterWhisper: class FasterWhisper:
def __init__(self):
pass
def recognize(self, model, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText): def recognize(self, model, audioFile, beamSize, vadFilter, minSilenceDurationMs, speechPadMs, temp0, temp1, temp2, wordTimestamps, noSpeechThreshold, conditionOnPreviousText):
model = WhisperModel(model, device='cuda', compute_type='float16') # Задаем модель model = WhisperModel(model, device='cuda', compute_type='float16') # Задаем модель