app.py

from fastapi import FastAPI, UploadFile, File, Form
import tempfile
import shutil
import uvicorn
import whisperx
import torch
import numpy as np
import soundfile as sf
from speechbrain.pretrained import EncoderClassifier

app = FastAPI()

device = "cpu"

# Load models (light)
asr_model = whisperx.load_model("small", device)

speaker_model = EncoderClassifier.from_hparams(
    source="speechbrain/spkrec-ecapa-voxceleb",
    run_opts={"device": device}
)

@app.post("/transcribe")
async def transcribe(audio: UploadFile = File(...), lang: str = Form("en")):

    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
    with temp as buffer:
        shutil.copyfileobj(audio.file, buffer)

    audio_path = temp.name

    # Load audio
    audio_data = whisperx.load_audio(audio_path)

    # Transcribe
    result = asr_model.transcribe(audio_data, language=lang)
    segments = result["segments"]

    y, sr = sf.read(audio_path)

    speaker_embeddings = []
    speaker_labels = []

    final_segments = []

    for i, seg in enumerate(segments):

        start = int(seg["start"] * sr)
        end = int(seg["end"] * sr)

        chunk = y[start:end]

        if len(chunk) < sr * 0.5:  # skip very short
            continue

        chunk_tensor = torch.tensor(chunk).unsqueeze(0)

        emb = speaker_model.encode_batch(chunk_tensor)
        emb = emb.squeeze().detach().cpu().numpy()

        # Assign speakers
        if len(speaker_embeddings) < 2:
            speaker_id = f"SPEAKER_{len(speaker_embeddings)+1}"
            speaker_embeddings.append(emb)
            speaker_labels.append(speaker_id)
        else:
            sims = []
            for e in speaker_embeddings:
                sim = np.dot(emb, e) / (
                    np.linalg.norm(emb) * np.linalg.norm(e)
                )
                sims.append(sim)

            speaker_id = speaker_labels[np.argmax(sims)]

        final_segments.append({
            "speaker": speaker_id,
            "start": round(seg["start"], 2),
            "end": round(seg["end"], 2),
            "text": seg["text"]
        })

    return {"segments": final_segments}


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)