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Runtime error
Pranjal12345
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ed343f7
1
Parent(s):
a3d265e
dsds
Browse files- main.py +120 -14
- requirements.txt +2 -1
main.py
CHANGED
@@ -1,36 +1,142 @@
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#uvicorn app:app --host 0.0.0.0 --port 8000 --reload
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from fastapi import FastAPI
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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import librosa
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import uvicorn
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app = FastAPI()
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processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
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model.config.forced_decoder_ids = None
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audio_file_path = "output.mp3"
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audio_data, _ = librosa.load(audio_file_path, sr=16000)
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@app.get("/")
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def
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input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
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predicted_ids = model.generate(input_features)
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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return {"transcription": transcription[0]}
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#
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# import uvicorn
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# uvicorn.run(app, host="0.0.0.0", port=8000)
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if __name__=='__main__':
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uvicorn.run('main:app', reload=True)
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#uvicorn app:app --host 0.0.0.0 --port 8000 --reload
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# from fastapi import FastAPI
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# from transformers import WhisperProcessor, WhisperForConditionalGeneration
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# import librosa
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# import uvicorn
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# app = FastAPI()
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# processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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# model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
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# model.config.forced_decoder_ids = None
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# audio_file_path = "output.mp3"
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# audio_data, _ = librosa.load(audio_file_path, sr=16000)
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# @app.get("/")
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# def transcribe_audio():
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# input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
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# predicted_ids = model.generate(input_features)
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# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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# return {"transcription": transcription[0]}
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# if __name__ == "__main__":
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# import uvicorn
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# uvicorn.run(app, host="0.0.0.0", port=8000)
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# if __name__=='__main__':
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# uvicorn.run('main:app', reload=True)
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#uvicorn app:app --host 0.0.0.0 --port 8000 --reload
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#curl -X GET "http://localhost:8000/?text=I%20like%20Apples"
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#http://localhost:8000/?text=I%20like%20Apples
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# from fastapi import FastAPI
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# from transformers import WhisperProcessor, WhisperForConditionalGeneration
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# import librosa
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# import uvicorn
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# app = FastAPI()
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# # Load model and processor
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# processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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# model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
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# model.config.forced_decoder_ids = None
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# # Path to your audio file
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# audio_file_path = "/home/pranjal/Downloads/output.mp3"
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# # Read the audio file
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# audio_data, _ = librosa.load(audio_file_path, sr=16000)
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# @app.get("/")
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# def transcribe_audio():
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# # Process the audio data using the Whisper processor
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# input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
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# # Generate transcription
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# predicted_ids = model.generate(input_features)
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# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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# return {"transcription": transcription[0]}
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# if __name__ == "__main__":
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# import uvicorn
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# uvicorn.run(app, host="0.0.0.0", port=8000)
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# if __name__=='__app__':
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# uvicorn.run('main:app', reload=True)
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from fastapi import FastAPI, UploadFile, File
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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import librosa
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from fastapi.responses import HTMLResponse
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import uvicorn
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import io
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app = FastAPI()
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# Load model and processor
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processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
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model.config.forced_decoder_ids = None
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@app.get("/")
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def read_root():
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html_form = """
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<html>
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<body>
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<h2>ASR Transcription</h2>
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<form action="/transcribe" method="post" enctype="multipart/form-data">
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<label for="audio_file">Upload an audio file (MP3 or WAV):</label>
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<input type="file" id="audio_file" name="audio_file" accept=".mp3, .wav" required><br><br>
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<input type="submit" value="Transcribe">
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</form>
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</body>
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</html>
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"""
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return HTMLResponse(content=html_form, status_code=200)
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@app.post("/transcribe")
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async def transcribe_audio(audio_file: UploadFile):
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try:
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# Read the uploaded audio file
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audio_data = await audio_file.read()
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# Process the audio data using the Whisper processor
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audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
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input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
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# Generate transcription
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predicted_ids = model.generate(input_features)
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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return {"transcription": transcription[0]}
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except Exception as e:
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return {"error": str(e)}
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# if __name__ == "__app__":
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# uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
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requirements.txt
CHANGED
@@ -4,4 +4,5 @@ datasets
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4 |
fastapi
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uvicorn
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transformers
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-
Torch
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fastapi
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uvicorn
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transformers
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Torch
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io
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