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import os
import torchaudio
import gradio as gr
import torch
from fastapi import FastAPI, HTTPException, File, UploadFile
from speechbrain.inference import SpeakerRecognition
from fastapi.responses import JSONResponse
import numpy as np
# Initialize the speaker verification model
speaker_verification = SpeakerRecognition.from_hparams(
source="speechbrain/spkrec-ecapa-voxceleb",
savedir="tmp_model"
)
# Function to calculate similarity score
def get_similarity(audio1, audio2, sample_rate=16000):
try:
# Ensure audio1 and audio2 are numpy arrays
signal1 = torch.tensor(audio1)
signal2 = torch.tensor(audio2)
# Make sure the signals are in the right shape (2D tensor: (1, N))
if signal1.ndimension() == 1:
signal1 = signal1.unsqueeze(0)
if signal2.ndimension() == 1:
signal2 = signal2.unsqueeze(0)
# Get similarity score and prediction
score, prediction = speaker_verification.verify_batch(signal1, signal2)
return float(score), "Yes" if prediction else "No"
except Exception as e:
return None, str(e) # Return error message if any exception
# API function to compare voices
def compare_voices(file1, file2):
try:
# Debugging: Check the types of inputs
print(f"Received file1: {type(file1)}")
print(f"Received file2: {type(file2)}")
# Ensure file1 and file2 are numpy arrays
if isinstance(file1, np.ndarray) and isinstance(file2, np.ndarray):
audio1, audio2 = file1, file2
else:
return {"error": "Invalid input format. Both inputs must be numpy arrays."}
# Get similarity score
score, is_same_user = get_similarity(audio1, audio2)
if score is None:
# Return the error message if processing fails
return {"error": is_same_user}
# Return a dictionary with the similarity score and prediction
return {"Similarity Score": f"{score:.4f}", "Same User Prediction": is_same_user}
except Exception as e:
# Handle unexpected errors
return {"error": str(e)}
# FastAPI app
app = FastAPI()
@app.post("/compare_voices/")
async def compare_voices_api(file1: UploadFile = File(...), file2: UploadFile = File(...)):
"""
Compare two audio files and return the similarity score and prediction.
"""
try:
# Process the audio files and return them as numpy arrays
file1_data = await file1.read()
file2_data = await file2.read()
# Assuming the audio is decoded into numpy arrays here (e.g., using torchaudio)
# For example:
audio1, _ = torchaudio.load(io.BytesIO(file1_data)) # (Tensor, sample_rate)
audio2, _ = torchaudio.load(io.BytesIO(file2_data)) # (Tensor, sample_rate)
audio1 = audio1.numpy()
audio2 = audio2.numpy()
# Compare the two audio files and return the result
return compare_voices(audio1, audio2)
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
# Gradio interface function
def gradio_interface():
return gr.Interface(
fn=compare_voices,
inputs=[
gr.Audio(type="numpy", label="First Audio File"), # Gradio now gives numpy arrays
gr.Audio(type="numpy", label="Second Audio File") # Gradio now gives numpy arrays
],
outputs="json", # Output results as JSON
live=False # No live interface, just the API
)
# Launch Gradio as a web interface
@app.on_event("startup")
async def startup():
gr.Interface(fn=compare_voices, inputs=[
gr.Audio(type="numpy", label="First Audio File"), # Gradio now gives numpy arrays
gr.Audio(type="numpy", label="Second Audio File") # Gradio now gives numpy arrays
], outputs="json", live=False).launch(share=True, inline=True)
# Running the FastAPI app with Gradio
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=5000)
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