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import gradio as gr
import transformers
import librosa
import torch
# Load the Shuka model pipeline.
pipe = transformers.pipeline(
model="sarvamai/shuka_v1",
trust_remote_code=True,
device=0 if torch.cuda.is_available() else -1,
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else None
)
def process_audio(audio):
"""
Processes the input audio and returns a text response generated by the Shuka model.
"""
if audio is None:
return "No audio provided."
# Gradio returns a tuple (sample_rate, numpy_array)
sample_rate, audio_data = audio
# Resample to 16000 Hz if necessary
if sample_rate != 16000:
audio_data = librosa.resample(audio_data, orig_sr=sample_rate, target_sr=16000)
sample_rate = 16000
# Define conversation turns with a system prompt and a user prompt that signals audio input
turns = [
{'role': 'system', 'content': 'Respond naturally and informatively.'},
{'role': 'user', 'content': '<|audio|>'}
]
# Run the pipeline with the audio input and conversation context
result = pipe({'audio': audio_data, 'turns': turns, 'sampling_rate': sample_rate}, max_new_tokens=512)
# Extract the generated text response
if isinstance(result, list) and len(result) > 0:
response = result[0].get('generated_text', '')
else:
response = str(result)
return response
# Create the Gradio interface without the 'source' parameter.
iface = gr.Interface(
fn=process_audio,
inputs=gr.Audio(type="numpy"),
outputs="text",
title="Sarvam AI Shuka Voice Demo",
description="Upload a voice note and get a response using Sarvam AI's Shuka model."
)
if __name__ == "__main__":
iface.launch()
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