56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
import whisper
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import time
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from google.cloud import speech_v1
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from google.cloud.speech_v1 import types
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import json
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from fastapi import FastAPI
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from pydantic import BaseModel
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app = FastAPI()
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@app.get("/")
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def read_main():
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return {"message": "Ok"}
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class Response(BaseModel):
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path: str
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model = whisper.load_model("medium")
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@app.post("/voice2txt/")
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def calculate_api(response: Response):
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path = response.path
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t=time.time()
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result = model.transcribe(path)["text"]
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return {"message": result,"time":time.time()-t}
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@app.post("/voice2txtGoogle/")
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def calculate_api_g(response: Response):
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path = response.path
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t=time.time()
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result = transcribe_ogg_audio(path)
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return {"message": result,"time":time.time()-t}
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#import argparse
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import os
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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "/home/mario/.config/gcloud/application_default_credentials.json"
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def transcribe_ogg_audio(audio_file_path, language_code='es-US'):
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client = speech_v1.SpeechClient()
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with open(audio_file_path, 'rb') as audio_file:
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content = audio_file.read()
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audio = speech_v1.RecognitionAudio(content=content)
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config = speech_v1.RecognitionConfig(
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encoding=types.RecognitionConfig.AudioEncoding.FLAC, # Use OGG encoding
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sample_rate_hertz=44100, # Update this to match your audio file
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language_code=language_code,
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)
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response = client.recognize(config=config, audio=audio)
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return list(response.results[0].alternatives)[0].transcript
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#results.alternatives[0].transcript |