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gradio demo
Browse files- app.py +46 -0
- requirements.txt +3 -0
app.py
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from transformers import pipeline
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import gradio as gr
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from pydub import AudioSegment
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import os
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# Initialize the pipeline
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pipe = pipeline(model="srirama/whisper-small-hi") # change to "your-username/the-name-you-picked"
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# Function to split audio into chunks
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def split_audio(audio_path, chunk_length_ms):
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audio = AudioSegment.from_file(audio_path)
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chunks = []
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for i in range(0, len(audio), chunk_length_ms):
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chunk = audio[i:i + chunk_length_ms]
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chunks.append(chunk)
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return chunks
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# Function to transcribe audio
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def transcribe(audio_path):
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chunk_length_ms = 20 * 1000 # 20 seconds in milliseconds
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audio_chunks = split_audio(audio_path, chunk_length_ms)
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full_transcript = ""
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for i, chunk in enumerate(audio_chunks):
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temp_file = f"temp_chunk_{i}.wav"
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chunk.export(temp_file, format="wav")
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text = pipe(temp_file)["text"]
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full_transcript += text + " "
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os.remove(temp_file) # Clean up the temporary file
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return full_transcript.strip()
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iface = gr.Interface(
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fn=transcribe,
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inputs=gr.Audio(sources=["microphone"], type="filepath"),
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outputs="text",
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title="Whisper Small English",
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description="Realtime demo for Dental speech recognition using a fine-tuned Whisper small model.",
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)
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# Launch the interface
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iface.launch()
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requirements.txt
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transformers==4.28.1
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pydub==0.25.1
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gradio==2.9.1
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