Spaces:
Running
Running
File size: 1,716 Bytes
cb9846d 13119ab a295231 086ae79 cb9846d c94a411 30214b5 a12b80a 30214b5 be89c7c c94a411 96f7c65 cb9846d 086ae79 07b76a5 cb9846d 96f7c65 d88e594 96f7c65 cb9846d 086ae79 96f7c65 cb9846d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import gradio as gr
from transformers import pipeline
import numpy as np
from google.cloud import speech_v1
from google.protobuf import timestamp_pb2
import os
"""Lista los archivos en la carpeta de ejecución."""
archivos = os.listdir()
print("\n".join(archivos))
print(os.getcwd())
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.getcwd().join("deploygpt-e9475e7c2c7c.json")
#transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
#def transcribe(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(audio_bytes):
"""Transcribe audio bytes to text using Google Cloud Speech to Text."""
# Crea un cliente de Speech to Text
client = speech_v1.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
# Crea una solicitud de reconocimiento de audio
audio = speech_v1.RecognitionAudio(content=audio_bytes)
request = speech_v1.RecognizeSpeechRequest(config=config, audio=audio)
# 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,
gr.Audio(sources=["microphone"], streaming=False),
"text",
#live=True, # No muestra el botón de Submit.
)
demo.launch()
|