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| import gradio as gr | |
| from transformers import pipeline | |
| import numpy as np | |
| #from google.cloud import speech_v1 | |
| from google.cloud import speech | |
| from google.protobuf import timestamp_pb2 | |
| import io | |
| import os | |
| """Lista los archivos en la carpeta de ejecución.""" | |
| archivos = os.listdir() | |
| print("\n".join(archivos)) | |
| print(os.getcwd()) | |
| rutas = [os.getcwd(),"deploygpt-e9475e7c2c7c.json"] | |
| print('/'.join(rutas)) | |
| os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = '/'.join(rutas) | |
| transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") | |
| def transcribe(audio_bytes): | |
| print(type(audio_bytes)) | |
| """Transcribe audio bytes to text using Google Cloud Speech to Text.""" | |
| sr, y = audio_bytes | |
| y = y.astype(np.float32) | |
| y /= np.max(np.abs(y)) | |
| return transcriber({"sampling_rate": sr, "raw": y})["text"] | |
| def transcribe_2(audio_bytes): | |
| """Transcribe audio bytes to text using Google Cloud Speech to Text.""" | |
| # Crea un cliente de Speech to Text | |
| #client = speech_v1.SpeechClient() | |
| client = speech.SpeechClient() | |
| # Configura la configuración de la solicitud | |
| #config = speech_v1.RecognitionConfig() | |
| #config.language_code = "es-AR" | |
| #config.encoding = speech_v1.RecognitionConfig.Encoding.LINEAR16 | |
| #config.sample_rate_hertz = 16000 | |
| config = speech.RecognitionConfig( | |
| encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16, | |
| enable_automatic_punctuation=True, | |
| audio_channel_count=1, | |
| language_code="es-AR", | |
| ) | |
| # Crea una solicitud de reconocimiento de audio | |
| #audio = speech_v1.RecognitionAudio(content=audio_bytes) | |
| #request = speech_v1.RecognizeSpeechRequest(config=config, audio=audio) | |
| print(f"{type(audio_bytes)} {audio_bytes}") | |
| file_name = audio_bytes | |
| #sr, y = audio_bytes | |
| #print(f"{type(sr)} {sr}") | |
| #print(type(y)) | |
| #y = y.astype(np.float32) | |
| #y /= np.max(np.abs(y)) | |
| #import scipy.io.wavfile as wav | |
| #RATE = sr | |
| #numpydata = y | |
| #file_name = 'out.wav' | |
| #wav.write(file_name, RATE, numpydata) | |
| #the path of your audio file | |
| with io.open(file_name, "rb") as audio_file: | |
| content = audio_file.read() | |
| audio = speech.RecognitionAudio(content=content) | |
| #audio = speech.RecognitionAudio(content=audio_bytes) | |
| # Sends the request to google to transcribe the audio | |
| response = client.recognize(request={"config": config, "audio": audio}) | |
| transcript = [] | |
| # Reads the response | |
| for result in response.results: | |
| print("Transcript: {}".format(result.alternatives[0].transcript)) | |
| transcript.append(result.alternatives[0].transcript) | |
| # Realiza la transcripción | |
| #response = client.recognize_speech(request) | |
| # Extrae el texto transcrito | |
| #transcript = response.results[0].alternatives[0].transcript | |
| return transcript | |
| demo = gr.Interface( | |
| transcribe_2, | |
| gr.Audio(sources=["microphone"], type="filepath", streaming=False), | |
| "text", | |
| #live=True, # No muestra el botón de Submit. | |
| ) | |
| demo.launch() | |