speech_edit.py

#!/usr/bin/env python3
# coding: utf‑8
"""
CosyVoice gRPC back‑end – updated to mirror the FastAPI logic
*   loads CosyVoice2 with TRT / FP16 first (falls back to CosyVoice)
*   inference_zero_shot  ➜  adds   stream=False   +   speed
*   inference_instruct   ➜  keeps original “speaker‑ID” path
*   inference_instruct2  ➜  new:  prompt‑audio + speed (no speaker‑ID)
"""

import io, tempfile, requests, soundfile as sf, torchaudio
import os
import sys
from concurrent import futures
import argparse
import logging
import grpc
import numpy as np
import torch

import cosyvoice_pb2
import cosyvoice_pb2_grpc

# ────────────────────────────────────────────────────────────────────────────────
# set‑up
# ────────────────────────────────────────────────────────────────────────────────
logging.getLogger("matplotlib").setLevel(logging.WARNING)
logging.basicConfig(level=logging.INFO,
                    format="%(asctime)s %(levelname)s %(message)s")

ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.extend([
    f"{ROOT_DIR}/../../..",
    f"{ROOT_DIR}/../../../third_party/Matcha-TTS",
])

from cosyvoice.cli.cosyvoice import CosyVoice2          # noqa: E402


# ────────────────────────────────────────────────────────────────────────────────
# helpers
# ────────────────────────────────────────────────────────────────────────────────
def _bytes_to_tensor(wav_bytes: bytes) -> torch.Tensor:
    """
    Convert int16 little‑endian PCM bytes → torch.FloatTensor in range [‑1,1]
    """
    speech = torch.from_numpy(
        np.frombuffer(wav_bytes, dtype=np.int16)
    ).unsqueeze(0).float() / (2 ** 15)
    return speech                                                      # [1, T]


def _yield_audio(model_output):
    """
    Generator that converts CosyVoice output → protobuf Response messages.
    """
    for seg in model_output:
        pcm16 = (seg["tts_speech"].numpy() * (2 ** 15)).astype(np.int16)
        resp = cosyvoice_pb2.Response(tts_audio=pcm16.tobytes())
        yield resp


# ────────────────────────────────────────────────────────────────────────────────
# gRPC service
# ────────────────────────────────────────────────────────────────────────────────
class CosyVoiceServiceImpl(cosyvoice_pb2_grpc.CosyVoiceServicer):
    def __init__(self, args):
        # try CosyVoice2 first (preferred runtime: TRT / FP16)
        try:
            self.cosyvoice = CosyVoice2(args.model_dir,
                                        load_jit=False,
                                        load_trt=True,
                                        fp16=True)
            logging.info("Loaded CosyVoice2 (TRT / FP16).")
        except Exception:
            raise TypeError("No valid CosyVoice model found!")

    # ---------------------------------------------------------------------
    # single bi‑di streaming RPC
    # ---------------------------------------------------------------------
    def Inference(self, request, context):
        """Route to the correct model call based on the oneof field present."""
        # 1. Supervised fine‑tuning
        if request.HasField("sft_request"):
            logging.info("Received SFT inference request")
            mo = self.cosyvoice.inference_sft(
                request.sft_request.tts_text,
                request.sft_request.spk_id
            )
            yield from _yield_audio(mo)
            return

        # 2. Zero‑shot speaker cloning  (bytes OR S3 URL)
        if request.HasField("zero_shot_request"):
            logging.info("Received zero‑shot inference request")
            zr = request.zero_shot_request
            tmp_path = None  # initialise so we can delete later
        
            try:
                # ───── determine payload type ──────────────────────────────────────
                if zr.prompt_audio.startswith(b'http'):
                    # —— remote URL —— ---------------------------------------------
                    url = zr.prompt_audio.decode('utf‑8')
                    logging.info("Downloading prompt audio from %s", url)
                    resp = requests.get(url, timeout=10)
                    resp.raise_for_status()
        
                    # save to a temp file
                    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
                        f.write(resp.content)
                        tmp_path = f.name
        
                    # load, mono‑ise, resample → tensor [1, T]
                    wav, sr = sf.read(tmp_path, dtype="float32")
                    if wav.ndim > 1:
                        wav = wav.mean(axis=1)
                    if sr != 16_000:
                        wav = torchaudio.functional.resample(
                            torch.from_numpy(wav).unsqueeze(0), sr, 16_000
                        )[0].numpy()
                    prompt = torch.from_numpy(wav).unsqueeze(0)
        
                else:
                    # —— legacy raw PCM bytes —— -----------------------------------
                    prompt = _bytes_to_tensor(zr.prompt_audio)
        
                # ───── call the model ──────────────────────────────────────────────
                speed = getattr(zr, "speed", 1.0)
                mo = self.cosyvoice.inference_zero_shot(
                    zr.tts_text,
                    zr.prompt_text,
                    prompt,
                    stream=False,
                    speed=speed,
                )
          
            finally:
                # clean up any temporary file we created
                if tmp_path and os.path.exists(tmp_path):
                    try:
                        os.remove(tmp_path)
                    except Exception as e:
                        logging.warning("Could not remove temp file %s: %s", tmp_path, e)

            yield from _yield_audio(mo)
            return
      
        # 3. Cross‑lingual
        if request.HasField("cross_lingual_request"):
            logging.info("Received cross‑lingual inference request")
            cr = request.cross_lingual_request
            tmp_path = None
        
            try:
                if cr.prompt_audio.startswith(b'http'):          # S3 URL case
                    url = cr.prompt_audio.decode('utf‑8')
                    logging.info("Downloading cross‑lingual prompt from %s", url)
                    resp = requests.get(url, timeout=10)
                    resp.raise_for_status()
        
                    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
                        f.write(resp.content)
                        tmp_path = f.name
        
                    wav, sr = sf.read(tmp_path, dtype='float32')
                    if wav.ndim > 1:
                        wav = wav.mean(axis=1)
                    if sr != 16_000:
                        wav = torchaudio.functional.resample(
                            torch.from_numpy(wav).unsqueeze(0), sr, 16_000
                        )[0].numpy()
                    prompt = torch.from_numpy(wav).unsqueeze(0)
        
                else:                                           # legacy raw bytes
                    prompt = _bytes_to_tensor(cr.prompt_audio)
        
                mo = self.cosyvoice.inference_cross_lingual(
                    cr.tts_text,
                    prompt
                )
        
            finally:
                if tmp_path and os.path.exists(tmp_path):
                    try:
                        os.remove(tmp_path)
                    except Exception as e:
                        logging.warning("Could not remove temp file %s: %s",
                                        tmp_path, e)
        
            yield from _yield_audio(mo)
            return


        # 4. Instruction‑TTS (two flavours)
        if request.HasField("instruct_request"):
            ir = request.instruct_request
        
            # ──────────────────────────────────────────────────────────────────
            # 4‑a) instruct‑2  (has prompt_audio  →  bytes OR S3 URL)
            # ──────────────────────────────────────────────────────────────────
            if ir.HasField("prompt_audio"):
                logging.info("Received instruct‑2 inference request")
        
                tmp_path = None
                try:
                    if ir.prompt_audio.startswith(b'http'):
                        # treat as URL, download then load
                        url = ir.prompt_audio.decode('utf‑8')
                        logging.info("Downloading prompt audio from %s", url)
                        resp = requests.get(url, timeout=10)
                        resp.raise_for_status()
        
                        with tempfile.NamedTemporaryFile(delete=False,
                                                         suffix=".wav") as f:
                            f.write(resp.content)
                            tmp_path = f.name
        
                        wav, sr = sf.read(tmp_path, dtype='float32')
                        if wav.ndim > 1:
                            wav = wav.mean(axis=1)
                        if sr != 16_000:
                            wav = torchaudio.functional.resample(
                                torch.from_numpy(wav).unsqueeze(0), sr, 16_000
                            )[0].numpy()
                        prompt = torch.from_numpy(wav).unsqueeze(0)
        
                    else:
                        # legacy raw‑bytes payload
                        prompt = _bytes_to_tensor(ir.prompt_audio)
        
                    speed = getattr(ir, "speed", 1.0)
                    mo = self.cosyvoice.inference_instruct2(
                        ir.tts_text,
                        ir.instruct_text,
                        prompt,
                        stream=False,
                        speed=speed
                    )
        
                finally:
                    if tmp_path and os.path.exists(tmp_path):
                        try:
                            os.remove(tmp_path)
                        except Exception as e:
                            logging.warning("Could not remove temp file %s: %s",
                                            tmp_path, e)
        
            # ──────────────────────────────────────────────────────────────────
            # 4‑b) classic instruct (speaker‑ID, no prompt audio)
            # ──────────────────────────────────────────────────────────────────
            else:
                logging.info("Received instruct inference request")
                mo = self.cosyvoice.inference_instruct(
                    ir.tts_text,
                    ir.spk_id,
                    ir.instruct_text
                )
        
            yield from _yield_audio(mo)
            return

        # unknown request type
        context.abort(grpc.StatusCode.INVALID_ARGUMENT,
                      "Unsupported request type in oneof field.")


# ────────────────────────────────────────────────────────────────────────────────
# entry‑point
# ────────────────────────────────────────────────────────────────────────────────
def serve(args):
    server = grpc.server(
        futures.ThreadPoolExecutor(max_workers=args.max_conc),
        maximum_concurrent_rpcs=args.max_conc
    )
    cosyvoice_pb2_grpc.add_CosyVoiceServicer_to_server(
        CosyVoiceServiceImpl(args), server
    )
    server.add_insecure_port(f"0.0.0.0:{args.port}")
    server.start()
    logging.info("CosyVoice gRPC server listening on 0.0.0.0:%d", args.port)
    server.wait_for_termination()


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--port", type=int, default=8000)
    parser.add_argument("--max_conc", type=int, default=4,
                        help="maximum concurrent requests / threads")
    parser.add_argument("--model_dir", type=str,
                        default="pretrained_models/CosyVoice2-0.5B",
                        help="local path or ModelScope repo id")
    serve(parser.parse_args())