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# # #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
# # # from fastapi import FastAPI
# # # from transformers import WhisperProcessor, WhisperForConditionalGeneration
# # # import librosa
# # # import uvicorn
# # # app = FastAPI()
# # # processor = WhisperProcessor.from_pretrained("openai/whisper-small")
# # # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
# # # model.config.forced_decoder_ids = None
# # # audio_file_path = "output.mp3"
# # # audio_data, _ = librosa.load(audio_file_path, sr=16000)
# # # @app.get("/")
# # # def transcribe_audio():
# # # input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
# # # predicted_ids = model.generate(input_features)
# # # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
# # # return {"transcription": transcription[0]}
# # # if __name__ == "__main__":
# # # import uvicorn
# # # uvicorn.run(app, host="0.0.0.0", port=8000)
# # # if __name__=='__main__':
# # # uvicorn.run('main:app', reload=True)
# # #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
# # #curl -X GET "http://localhost:8000/?text=I%20like%20Apples"
# # #http://localhost:8000/?text=I%20like%20Apples
# # # from fastapi import FastAPI
# # # from transformers import WhisperProcessor, WhisperForConditionalGeneration
# # # import librosa
# # # import uvicorn
# # # app = FastAPI()
# # # # Load model and processor
# # # processor = WhisperProcessor.from_pretrained("openai/whisper-small")
# # # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
# # # model.config.forced_decoder_ids = None
# # # # Path to your audio file
# # # audio_file_path = "/home/pranjal/Downloads/output.mp3"
# # # # Read the audio file
# # # audio_data, _ = librosa.load(audio_file_path, sr=16000)
# # # @app.get("/")
# # # def transcribe_audio():
# # # # Process the audio data using the Whisper processor
# # # input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
# # # # Generate transcription
# # # predicted_ids = model.generate(input_features)
# # # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
# # # return {"transcription": transcription[0]}
# # # if __name__ == "__main__":
# # # import uvicorn
# # # uvicorn.run(app, host="0.0.0.0", port=8000)
# # # if __name__=='__app__':
# # # uvicorn.run('main:app', reload=True)
# # from fastapi import FastAPI, UploadFile, File
# # from transformers import WhisperProcessor, WhisperForConditionalGeneration
# # import librosa
# # from fastapi.responses import HTMLResponse
# # import uvicorn
# # import io
# # app = FastAPI()
# # # Load model and processor
# # processor = WhisperProcessor.from_pretrained("openai/whisper-small")
# # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
# # model.config.forced_decoder_ids = None
# # @app.get("/")
# # def read_root():
# # html_form = """
# # <html>
# # <body>
# # <h2>ASR Transcription</h2>
# # <form action="/transcribe" method="post" enctype="multipart/form-data">
# # <label for="audio_file">Upload an audio file (MP3 or WAV):</label>
# # <input type="file" id="audio_file" name="audio_file" accept=".mp3, .wav" required><br><br>
# # <input type="submit" value="Transcribe">
# # </form>
# # </body>
# # </html>
# # """
# # return HTMLResponse(content=html_form, status_code=200)
# # @app.post("/transcribe")
# # async def transcribe_audio(audio_file: UploadFile):
# # try:
# # # Read the uploaded audio file
# # audio_data = await audio_file.read()
# # # Process the audio data using the Whisper processor
# # audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
# # input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
# # # Generate transcription
# # predicted_ids = model.generate(input_features)
# # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
# # return {"transcription": transcription[0]}
# # except Exception as e:
# # return {"error": str(e)}
# # if __name__ == "__app__":
# # uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
# #uvicorn app:app --host 0.0.0.0 --port 8000 --reload
# from fastapi import FastAPI, UploadFile, File
# from transformers import WhisperProcessor, WhisperForConditionalGeneration
# import librosa
# from fastapi.responses import HTMLResponse
# import uvicorn
# import io
# app = FastAPI()
# # # Load model and processor
# # processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
# # model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium")
# # model.config.forced_decoder_ids = None
# import whisper
# model = whisper.load_model("small")
# @app.get("/")
# def read_root():
# html_form = """
# <html>
# <body>
# <h2>ASR Transcription</h2>
# <form action="/transcribe" method="post" enctype="multipart/form-data">
# <label for="audio_file">Upload an audio file (MP3 or WAV):</label>
# <input type="file" id="audio_file" name="audio_file" accept=".mp3, .wav" required><br><br>
# <input type="submit" value="Transcribe">
# </form>
# </body>
# </html>
# """
# return HTMLResponse(content=html_form, status_code=200)
# @app.post("/transcribe")
# async def transcribe_audio(audio_file: UploadFile):
# try:
# # Read the uploaded audio file
# audio_data = await audio_file.read()
# # Process the audio data using the Whisper processor
# # audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
# # input_features = processor(audio_data.tolist(), return_tensors="pt").input_features
# # # Generate transcription
# # predicted_ids = model.generate(input_features)
# # transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
# result = model.transcribe("/home/pranjal/Downloads/rt.mp3")
# return {"transcription": result['text']}
# except Exception as e:
# return {"error": str(e)}
# # if __name__ == "__app__":
# # uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
#uvicorn app:app --host 0.0.0.0 --port 8000 --reload
from fastapi import FastAPI, UploadFile, File
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from fastapi.responses import HTMLResponse
import librosa
import io
import re
html_tag_remover = re.compile(r'<[^>]+>')
def remove_tags(text):
return html_tag_remover.sub('', text)
app = FastAPI()
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
model.config.forced_decoder_ids = None
chunk_duration = 30
overlap_duration = 5
@app.get("/")
def read_root():
html_form = """
<html>
<body>
<h2>ASR Transcription</h2>
<form action="/transcribe" method="post" enctype="multipart/form-data">
<label for="audio_file">Upload an audio file (MP3 or WAV):</label>
<input type="file" id="audio_file" name="audio_file" accept=".mp3, .wav" required><br><br>
<input type="submit" value="Transcribe">
</form>
</body>
</html>
"""
return HTMLResponse(content=html_form, status_code=200)
@app.post("/transcribe")
async def transcribe_audio(audio_file: UploadFile):
audio_data = await audio_file.read()
audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000)
transcription = []
start = 0
while start < len(audio_data):
end = start + chunk_duration * 16000
audio_chunk = audio_data[start:end]
input_features = processor(audio_chunk.tolist(), return_tensors="pt").input_features
predicted_ids = model.generate(input_features, max_length=1000)
chunk_transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
transcription.extend(chunk_transcription)
start = end - overlap_duration * 16000
final_transcription = " ".join(transcription)
final_transcription = remove_tags(final_transcription)
return {"transcription": final_transcription}
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