File size: 1,549 Bytes
a01445b |
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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import os
from transformers import pipeline
from huggingface_hub import InferenceClient
import gradio as gr
# Initialize the translation pipeline
translation_pipeline = pipeline("translation", model="facebook/nllb-200-distilled-600M")
# Initialize the text-to-speech client
# Make sure you have HF_TOKEN set as a secret in your Hugging Face Space
client = InferenceClient(
provider="fal-ai",
api_key=os.environ.get("HF_TOKEN"),
)
def translate_and_speak(chinese_text):
"""
Translates Chinese text to English and generates speech.
"""
# Translate the text
translated_text = translation_pipeline(chinese_text)[0]['translation_text']
# Generate speech from the translated text
# Use a try-except block to handle potential errors during speech generation
try:
audio_bytes = client.text_to_speech(
translated_text,
model="hexgrad/Kokoro-82M",
)
return translated_text, audio_bytes
except Exception as e:
return translated_text, f"Error generating speech: {e}"
# Create the Gradio interface
iface = gr.Interface(
fn=translate_and_speak,
inputs=gr.Textbox(label="Enter Chinese Text"),
outputs=[
gr.Textbox(label="Translated English Text"),
gr.Audio(label="Generated Speech", format="wav")
],
title="Chinese to English Translation and Text-to-Speech",
description="Translate Chinese text to English and listen to the English translation."
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch() |