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import torch
import torchaudio
import soundfile as sf
import gradio as gr
from transformers import SeamlessM4TProcessor, SeamlessM4TModel
# Load model and processor
HF_MODEL_ID = "facebook/hf-seamless-m4t-medium"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = SeamlessM4TProcessor.from_pretrained(HF_MODEL_ID)
model = SeamlessM4TModel.from_pretrained(HF_MODEL_ID).to(device).eval()
def translate_backend(audio_path, src_lang, tgt_lang):
try:
waveform, sr = sf.read(audio_path)
if len(waveform.shape) > 1:
waveform = waveform.mean(axis=1)
waveform = waveform.astype("float32")
if sr != 16000:
waveform_tensor = torch.tensor(waveform).unsqueeze(0)
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)
waveform = resampler(waveform_tensor).squeeze(0).numpy()
sr = 16000
inputs = processor(audios=waveform, sampling_rate=sr, return_tensors="pt", src_lang=src_lang).to(device)
with torch.no_grad():
output = model.generate(**inputs, tgt_lang=tgt_lang, generate_speech=False)
translated_text = processor.batch_decode(output.sequences, skip_special_tokens=True)[0]
return translated_text
except Exception as e:
return f"❌ Error: {str(e)}"
# Standard Gradio launch (no API flag)
iface = gr.Interface(
fn=translate_backend,
inputs=[
gr.Audio(type="filepath", label="🎤 Audio"),
gr.Text(label="Source Language"),
gr.Text(label="Target Language")
],
outputs="text",
title="Kalpani iVoice Translate",
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch() # No 'api=True'
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