Update app.py

app.py

@@ -1,21 +1,38 @@
 import gradio as gr
-from transformers import pipeline
-import numpy as np
+from google.cloud import speech_v1
+from google.protobuf import timestamp_pb2
 
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
 
-def transcribe(audio):
-    sr, y = audio
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
+def transcribe(audio_bytes):
+    """Transcribe audio bytes to text using Google Cloud Speech to Text."""
 
-    return transcriber({"sampling_rate": sr, "raw": y})["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-ES"
+    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"]),
+    gr.Audio(sources=["microphone"], streaming=True),
     "text",
+    live=True,
 )
 
 demo.launch()