Update app.py
Browse files
app.py
CHANGED
@@ -12,7 +12,25 @@ import pynvml
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logging.basicConfig(level=logging.INFO)
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last_model = None
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model = None
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def get_free_gpu_memory():
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pynvml.nvmlInit()
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@@ -99,62 +117,31 @@ def transcribe_webui_simple_progress(modelName, languageName, urlData, multipleF
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return files_out, vtt, txt
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# task = gr.Dropdown(choices=["transcribe", "translate"], label="Task", value="transcribe", interactive = True),
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# chunk_length = gr.Number(label='chunk_length',value=30, interactive = True),
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# compute_type = gr.Dropdown(label="compute_type", choices=compute_types, value="auto", interactive = True),
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# beam_size = gr.Number(label='beam_size',value=5, interactive = True),
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# vad_filter = gr.Checkbox(label='vad_filter',info='Use vad_filter', value=True),
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# vad_min_silence_duration_ms = gr.Number(label='Vad min_silence_duration_ms',value=500, interactive = True),
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gr.Interface(
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fn=transcribe_webui_simple_progress,
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description=description,
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article=article,
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inputs=[
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gr.Dropdown(choices=whisper_models, value="distil-large-v2", label="Model", info="Select whisper model", interactive = True,),
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gr.Dropdown(choices=["Automatic Detection"] + sorted(get_language_names()), value="Automatic Detection", label="Language", info="Select audio voice language", interactive = True,),
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gr.Text(label="URL", info="(YouTube, etc.)", interactive = True),
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gr.File(label="Upload Files", file_count="multiple"),
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gr.Audio(sources=["upload", "microphone"], type="filepath", label="Input Audio"),
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gr.Dropdown(choices=["transcribe", "translate"], label="Task", value="transcribe", interactive = True),
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gr.Number(label='chunk_length',value=30, interactive = True),
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gr.Dropdown(label="compute_type", choices=compute_types, value="auto", interactive = True),
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gr.Number(label='beam_size',value=5, interactive = True),
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gr.Checkbox(label='vad_filter',info='Use vad_filter', value=True),
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gr.Number(label='Vad min_silence_duration_ms',value=500, interactive = True),
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],
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outputs=[
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gr.File(label="Download"),
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gr.Text(label="Transcription"),
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gr.Text(label="Segments"),
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]
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)
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if __name__ == "__main__":
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demo.queue(default_concurrency_limit=get_workers_count())
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logging.basicConfig(level=logging.INFO)
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last_model = None
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model = None
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description = "faster-whisper is a reimplementation of OpenAI's Whisper model using CTranslate2, which is a fast inference engine for Transformer models."
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article = "Read the [documentation here](https://github.com/SYSTRAN/faster-whisper)."
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whisper_models = [
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"tiny", "tiny.en",
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"base", "base.en", "Systran/faster-whisper-base.en", "Systran/faster-whisper-base",
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"small", "small.en", "distil-small.en", "Systran/faster-distil-whisper-small.en",
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"medium", "medium.en", "distil-medium.en", "Systran/faster-distil-whisper-medium.en", "Systran/faster-whisper-medium",
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"large",
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"large-v1", "Systran/faster-whisper-large-v1",
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"large-v2", "distil-large-v2", "Systran/faster-distil-whisper-large-v2",
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"large-v3", "distil-large-v3", "Systran/faster-distil-whisper-large-v3", "distil-whisper/distil-large-v3-ct2",
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"ivrit-ai/whisper-large-v3-turbo-ct2", "deepdml/faster-whisper-large-v3-turbo-ct2",
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"distil-whisper/distil-large-v3.5-ct2",
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]
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compute_types = [
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"auto", "default", "int8", "int8_float32",
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"int8_float16", "int8_bfloat16", "int16",
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"float16", "float32", "bfloat16"
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]
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def get_free_gpu_memory():
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pynvml.nvmlInit()
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return files_out, vtt, txt
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demo = gr.Interface(
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fn=transcribe_webui_simple_progress,
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description=description,
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article=article,
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inputs=[
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gr.Dropdown(choices=whisper_models, value="distil-whisper/distil-large-v3.5-ct2", label="Model", info="Select whisper model", interactive = True,),
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gr.Dropdown(choices=["Automatic Detection"] + sorted(get_language_names()), value="Automatic Detection", label="Language", info="Select audio voice language", interactive = True,),
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gr.Text(label="URL", info="(YouTube, etc.)", interactive = True),
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gr.File(label="Upload Files", file_count="multiple"),
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gr.Audio(sources=["upload", "microphone"], type="filepath", label="Input Audio"),
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gr.Dropdown(choices=["transcribe", "translate"], label="Task", value="transcribe", interactive = True),
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gr.Number(label='chunk_length',value=30, interactive = True),
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gr.Dropdown(label="compute_type", choices=compute_types, value="auto", interactive = True),
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gr.Number(label='beam_size',value=5, interactive = True),
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gr.Checkbox(label='vad_filter',info='Use vad_filter', value=True),
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gr.Number(label='Vad min_silence_duration_ms',value=500, interactive = True),
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],
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outputs=[
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gr.File(label="Download"),
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gr.Text(label="Transcription"),
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gr.Text(label="Segments"),
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],
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title="Fast Whisper WebUI"
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)
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if __name__ == "__main__":
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demo.queue(default_concurrency_limit=get_workers_count())
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