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Update app.py
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app.py
CHANGED
@@ -6,16 +6,14 @@ import torch
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from huggingface_hub import hf_hub_download
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from speechbrain.inference.TTS import Tacotron2
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# Cargar Tacotron2
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tacotron2 = Tacotron2.from_hparams(
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source="speechbrain/tts-tacotron2-ljspeech",
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savedir="tmpdir_tts",
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run_opts={"device": "cpu"}
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)
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#
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# Cargar tu generator.keras desde HuggingFace
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# Cargar tu modelo generator.keras desde HuggingFace
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model_path = hf_hub_download(
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repo_id="Bmo411/WGAN",
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filename="generator_epoch_3500.keras"
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@@ -23,35 +21,36 @@ model_path = hf_hub_download(
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generator = keras.models.load_model(model_path, compile=False)
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# Funci贸n
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def text_to_audio(text):
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#
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mel_output, _, _ = tacotron2.encode_text(text)
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mel = mel_output.detach().cpu().numpy().astype(np.float32) # (80, frames)
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# 2. Preparar para generator
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mel_input = np.expand_dims(mel,axis=0) # (1, 80, frames, 1)
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mel_input = np.expand_dims(mel_input,axis=-1) # (1, 80, frames, 1)
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#
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fake_audio = generator(mel_input, training=False)
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fake_audio =
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#
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fake_audio = np.clip(fake_audio, -1.0, 1.0)
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#
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return fake_audio, 8000 #
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#
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interface = gr.Interface(
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fn=text_to_audio,
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inputs=gr.Textbox(lines=1, placeholder="Escribe
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outputs=gr.Audio(type="numpy", label="Audio generado"),
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title="Demo de TTS con Tacotron2 +
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description="Convierte texto en audio usando Tacotron2 + tu modelo
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)
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# Lanzar
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if __name__ == "__main__":
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interface.launch()
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from huggingface_hub import hf_hub_download
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from speechbrain.inference.TTS import Tacotron2
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# Cargar modelo Tacotron2
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tacotron2 = Tacotron2.from_hparams(
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source="speechbrain/tts-tacotron2-ljspeech",
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savedir="tmpdir_tts",
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run_opts={"device": "cpu"}
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)
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# Descargar y cargar el modelo Generator entrenado
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model_path = hf_hub_download(
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repo_id="Bmo411/WGAN",
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filename="generator_epoch_3500.keras"
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generator = keras.models.load_model(model_path, compile=False)
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# Funci贸n para convertir texto a audio
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def text_to_audio(text):
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# Convertir texto a mel-spectrograma
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mel_output, _, _ = tacotron2.encode_text(text)
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mel = mel_output.detach().cpu().numpy().astype(np.float32) # (80, frames)
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# Preparar mel para el generador
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mel_input = np.expand_dims(mel, axis=0) # (1, 80, frames)
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mel_input = np.expand_dims(mel_input, axis=-1) # (1, 80, frames, 1)
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# Generar audio
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fake_audio = generator(mel_input, training=False)
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fake_audio = tf.squeeze(fake_audio, axis=0).numpy() # (samples, 1)
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fake_audio = np.squeeze(fake_audio, axis=-1) # (samples,)
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# Asegurar que est茅 en rango [-1, 1]
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fake_audio = np.clip(fake_audio, -1.0, 1.0)
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# Retornar audio y sample rate
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return fake_audio, 8000 # Asumiendo salida a 8kHz
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# Crear interfaz en Gradio
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interface = gr.Interface(
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fn=text_to_audio,
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inputs=gr.Textbox(lines=1, placeholder="Escribe algo (ej. 'nine')"),
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outputs=gr.Audio(type="numpy", label="Audio generado"),
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title="Demo de TTS con Tacotron2 + Generador",
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description="Convierte texto en audio usando Tacotron2 + tu modelo Generator entrenado."
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)
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# Lanzar aplicaci贸n
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if __name__ == "__main__":
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interface.launch()
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