| <html> | |
| <head> | |
| <script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script> | |
| <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css" /> | |
| </head> | |
| <body> | |
| <gradio-lite> | |
| <gradio-requirements> | |
| transformers_js_py | |
| </gradio-requirements> | |
| <gradio-file name="app.py" entrypoint> | |
| from transformers_js import pipeline | |
| import gradio as gr | |
| import numpy as np | |
| import scipy.io.wavfile as wavfile | |
| speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin'; | |
| async def synthesize(text): | |
| synthesizer = await pipeline( | |
| 'text-to-speech', | |
| 'Xenova/speecht5_tts', | |
| { "quantized": False } | |
| ) # Put the pipeline initializer inside the function to show the first view of the app faster | |
| out = await synthesizer(text, { "speaker_embeddings": speaker_embeddings }); | |
| audio_data_memory_view = out["audio"] | |
| sampling_rate = out["sampling_rate"] | |
| audio_data = np.frombuffer(audio_data_memory_view, dtype=np.float32) | |
| wavfile.write('output.wav', sampling_rate, audio_data) | |
| return "output.wav" | |
| demo = gr.Interface(synthesize, "textbox", "audio") | |
| demo.launch() | |
| </gradio-file> | |
| </gradio-lite> | |
| </body> | |
| </html> | |