#!/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 import os, io, tempfile, requests, torch, torchaudio from urllib.parse import urlparse def _load_prompt_from_url(url: str, target_sr: int = 16_000) -> torch.Tensor: """Download an audio file from ``url`` (wav / mp3 / flac / ogg …), convert it to mono, resample to ``target_sr`` if necessary, and return a 1×T float‑tensor in the range ‑1…1.""" # ─── 1. Download ──────────────────────────────────────────────────────────── resp = requests.get(url, timeout=10) if resp.status_code != 200: raise HTTPException(status_code=400, detail=f"Failed to download audio from URL: {url}") # Infer extension from URL *or* Content‑Type header ext = os.path.splitext(urlparse(url).path)[1].lower() if not ext and 'content-type' in resp.headers: mime = resp.headers['content-type'].split(';')[0].strip() ext = { 'audio/mpeg': '.mp3', 'audio/wav': '.wav', 'audio/x-wav': '.wav', 'audio/flac': '.flac', 'audio/ogg': '.ogg', 'audio/x-m4a': '.m4a', }.get(mime, '.audio') # generic fallback with tempfile.NamedTemporaryFile(suffix=ext or '.audio', delete=False) as f: f.write(resp.content) temp_path = f.name # ─── 2. Decode (torchaudio first, pydub fallback) ────────────────────────── try: # Let torchaudio pick the right backend automatically speech, sample_rate = torchaudio.load(temp_path) except Exception: # Fallback that works as long as ffmpeg is present from pydub import AudioSegment import numpy as np seg = AudioSegment.from_file(temp_path) # any ffmpeg‑supported format seg = seg.set_channels(1) # force mono sample_rate = seg.frame_rate np_audio = np.array(seg.get_array_of_samples()).astype(np.float32) # normalise to −1…1 based on sample width np_audio /= float(1 << (8 * seg.sample_width - 1)) speech = torch.from_numpy(np_audio).unsqueeze(0) finally: os.unlink(temp_path) # ─── 3. Ensure mono + correct sample‑rate ────────────────────────────────── if speech.dim() > 1 and speech.size(0) > 1: speech = speech.mean(dim=0, keepdim=True) # average to mono if sample_rate != target_sr: speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech) return speech # ──────────────────────────────────────────────────────────────────────────────── # 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'): prompt = _load_prompt_from_url(zr.prompt_audio.decode('utf‑8')) 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 prompt = _load_prompt_from_url(cr.prompt_audio.decode('utf‑8')) 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. Instruct‑2 (CosyVoice2 supports this variant only) if request.HasField("instruct_request"): ir = request.instruct_request # ---- require that the descriptor contains the field ------------------- if 'prompt_audio' not in ir.DESCRIPTOR.fields_by_name: context.abort( grpc.StatusCode.INVALID_ARGUMENT, "Server expects instruct‑2 proto with a 'prompt_audio' field." ) # ---- make sure it is non‑empty (no HasField for proto3 scalars) ------- if len(ir.prompt_audio) == 0: context.abort( grpc.StatusCode.INVALID_ARGUMENT, "'prompt_audio' must not be empty for instruct‑2 requests." ) logging.info("Received instruct‑2 inference request") # convert to bytes no matter what scalar type the proto uses pa_bytes = (ir.prompt_audio.encode('utf-8') if isinstance(ir.prompt_audio, str) else ir.prompt_audio) # URL vs raw bytes if pa_bytes.startswith(b"http"): prompt = _load_prompt_from_url(pa_bytes.decode('utf-8')) else: prompt = _bytes_to_tensor(pa_bytes) speed = getattr(ir, "speed", 1.0) mo = self.cosyvoice.inference_instruct2( ir.tts_text, ir.instruct_text, prompt, stream=False, speed=speed, ) 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())