Fix code: define device, fix imports, add pipeline

app.py

@@ -1,54 +1,55 @@
 import torch
-from transformers import pipeline
+import numpy as np
+from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
 from datasets import load_dataset
+import gradio as gr
+
+# Configuración del dispositivo (GPU si está disponible)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Pipeline de traducción automática de voz
+pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if torch.cuda.is_available() else -1)
 
 def translate(audio):
-    outputs = pipe(audio, generate_kwargs={"task": "translate","max_new_tokens":256})
+    outputs = pipe(audio, generate_kwargs={"task": "translate", "max_new_tokens": 256})
     return outputs["text"]
 
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
-
+# Modelos para síntesis de voz
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
 vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
 
-model.to(device);
-vocoder.to(device);
+model.to(device)
+vocoder.to(device)
 
+# Embedding del hablante
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
 speaker_embeddings = torch.tensor(embeddings_dataset[6000]["xvector"]).unsqueeze(0)
-     
 
 def synthesise(text):
     inputs = processor(text=text, return_tensors="pt")
     speech = model.generate_speech(
-        inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder
+        inputs["input_ids"].to(device),
+        speaker_embeddings.to(device),
+        vocoder=vocoder
     )
     return speech.cpu()
 
-import numpy as np
-
+# Conversión final
 target_dtype = np.int16
 max_range = np.iinfo(target_dtype).max
 
-
 def speech_to_speech_translation(audio):
     translated_text = translate(audio)
     synthesised_speech = synthesise(translated_text)
     synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
     return 16000, synthesised_speech
-    
-
-import gradio as gr
 
+# Interfaz Gradio
 demo = gr.Interface(
-    # Indicamos la función que se usa para realizar las predicciones
     fn=speech_to_speech_translation,
-    # Le indicamos la entrada, en este caso será un audio grabado desde el micrófono
     inputs=gr.Audio(sources="microphone", type="filepath"),
-    # Le indicamos la salida, en este caso será un audio generado aplicando la función
-    # indicada en fn al audio de entrada
     outputs=gr.Audio(label="Generated Speech", type="numpy"),
 )
 
-demo.launch(debug=True)
+demo.launch(debug=True)