File size: 962 Bytes
94fb3e3 87b7198 94fb3e3 |
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 |
<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 import_transformers_js
import gradio as gr
speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
transformers = await import_transformers_js()
pipeline = transformers.pipeline
synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { "quantized": False })
async def classify(text):
return await synthesizer(text, { "speaker_embeddings": speaker_embeddings });
demo = gr.Interface(classify, "textbox", "audio")
demo.launch()
</gradio-file>
</gradio-lite>
</body>
</html>
|