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# Copyright 2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import json
import os
import logging
from typing import List, Dict
import torch
from torch.utils.dlpack import to_dlpack
from torch.nn import functional as F
import triton_python_backend_utils as pb_utils
from hyperpyyaml import load_hyperpyyaml
from cosyvoice.utils.common import fade_in_out
from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm
from cosyvoice.utils.common import TrtContextWrapper
from collections import defaultdict
import numpy as np
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
ORIGINAL_VOCAB_SIZE = 151663
torch.set_num_threads(1)
class CosyVoice2:
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, trt_concurrent=1, device='cuda'):
self.model_dir = model_dir
self.fp16 = fp16
hyper_yaml_path = '{}/cosyvoice2.yaml'.format(model_dir)
if not os.path.exists(hyper_yaml_path):
raise ValueError('{} not found!'.format(hyper_yaml_path))
with open(hyper_yaml_path, 'r') as f:
configs = load_hyperpyyaml(f, overrides={'qwen_pretrain_path': os.path.join(model_dir, 'CosyVoice-BlankEN')})
self.model = CosyVoice2Model(configs['flow'], configs['hift'], fp16, device)
self.model.load('{}/flow.pt'.format(model_dir), '{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
if load_trt:
self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
trt_concurrent,
self.fp16)
class CosyVoice2Model:
def __init__(self,
flow: torch.nn.Module,
hift: torch.nn.Module,
fp16: bool = False,
device: str = 'cuda'):
self.device = device
self.flow = flow
self.hift = hift
self.fp16 = fp16
if self.fp16 is True:
self.flow.half()
# streaming tts config
self.token_hop_len = 25
self.mel_cache_len = 8
self.source_cache_len = int(self.mel_cache_len * 480)
self.speech_window = np.hamming(2 * self.source_cache_len)
self.hift_cache_dict = defaultdict(lambda: None)
def load_jit(self, flow_encoder_model):
flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
self.flow.encoder = flow_encoder
def load(self, flow_model, hift_model):
self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
self.flow.to(self.device).eval()
# in case hift_model is a hifigan model
hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()}
self.hift.load_state_dict(hift_state_dict, strict=True)
self.hift.to(self.device).eval()
def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16):
assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0:
convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16)
del self.flow.decoder.estimator
import tensorrt as trt
with open(flow_decoder_estimator_model, 'rb') as f:
estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device)
def get_trt_kwargs(self):
min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)]
max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
input_names = ["x", "mask", "mu", "cond"]
return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
with torch.cuda.amp.autocast(self.fp16):
tts_mel, _ = self.flow.inference(token=token.to(self.device),
token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
prompt_token=prompt_token.to(self.device),
prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
prompt_feat=prompt_feat.to(self.device),
prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=embedding.to(self.device),
streaming=stream,
finalize=finalize)
tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
# append hift cache
if self.hift_cache_dict[uuid] is not None:
hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
else:
hift_cache_source = torch.zeros(1, 1, 0)
# keep overlap mel and hift cache
if finalize is False:
tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
'source': tts_source[:, :, -self.source_cache_len:],
'speech': tts_speech[:, -self.source_cache_len:]}
tts_speech = tts_speech[:, :-self.source_cache_len]
else:
if speed != 1.0:
assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
if self.hift_cache_dict[uuid] is not None:
tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
return tts_speech
class TritonPythonModel:
"""Triton Python model for vocoder.
This model takes global and semantic tokens as input and generates audio waveforms
using the BiCodec vocoder.
"""
def initialize(self, args):
"""Initialize the model.
Args:
args: Dictionary containing model configuration
"""
# Parse model parameters
parameters = json.loads(args['model_config'])['parameters']
model_params = {key: value["string_value"] for key, value in parameters.items()}
model_dir = model_params["model_dir"]
# Initialize device and vocoder
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.info(f"Initializing vocoder from {model_dir} on {self.device}")
self.token2wav_model = CosyVoice2(
model_dir, load_jit=False, load_trt=True, fp16=True, device=self.device
)
spk_info_path = os.path.join(model_dir, "spk2info.pt")
if not os.path.exists(spk_info_path):
raise ValueError(f"spk2info.pt not found in {model_dir}")
spk_info = torch.load(spk_info_path, map_location="cpu", weights_only=False)
self.default_spk_info = spk_info["001"]
logger.info("Token2Wav initialized successfully")
def execute(self, requests):
"""Execute inference on the batched requests.
Args:
requests: List of inference requests
Returns:
List of inference responses containing generated waveforms
"""
responses = []
# Process each request in batch
for request in requests:
target_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "target_speech_tokens").as_numpy()
target_speech_tokens = torch.from_numpy(target_speech_tokens_tensor).to(self.device)
prompt_speech_tokens_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_tokens")
if prompt_speech_tokens_tensor is not None:
prompt_speech_tokens_tensor = prompt_speech_tokens_tensor.as_numpy()
prompt_speech_feat_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_speech_feat").as_numpy()
prompt_spk_embedding_tensor = pb_utils.get_input_tensor_by_name(request, "prompt_spk_embedding").as_numpy()
prompt_speech_tokens = torch.from_numpy(prompt_speech_tokens_tensor).to(self.device)
prompt_speech_feat = torch.from_numpy(prompt_speech_feat_tensor).to(self.device)
prompt_spk_embedding = torch.from_numpy(prompt_spk_embedding_tensor).to(self.device)
prompt_speech_tokens = prompt_speech_tokens - ORIGINAL_VOCAB_SIZE
else:
prompt_speech_tokens = self.default_spk_info["speech_token"].to(self.device)
prompt_speech_feat = self.default_spk_info["speech_feat"].to(torch.float16).to(self.device)
prompt_spk_embedding = self.default_spk_info["embedding"].to(torch.float16).to(self.device)
# shift the speech tokens according to the original vocab size
target_speech_tokens = target_speech_tokens - ORIGINAL_VOCAB_SIZE
# We set token_offset as an optional input to support streaming/offline tts. It has to be None when offline tts.
token_offset = pb_utils.get_input_tensor_by_name(request, "token_offset")
if token_offset is not None:
token_offset = token_offset.as_numpy().item()
finalize = pb_utils.get_input_tensor_by_name(request, "finalize").as_numpy().item()
if not finalize:
stream = True
else:
stream = False
request_id = request.request_id()
audio_hat = self.token2wav_model.model.token2wav(token=target_speech_tokens,
prompt_token=prompt_speech_tokens,
prompt_feat=prompt_speech_feat,
embedding=prompt_spk_embedding,
token_offset=token_offset,
uuid=request_id,
stream=stream,
finalize=finalize)
if finalize:
self.token2wav_model.model.hift_cache_dict.pop(request_id)
else:
tts_mel, _ = self.token2wav_model.model.flow.inference(
token=target_speech_tokens,
token_len=torch.tensor([target_speech_tokens.shape[1]], dtype=torch.int32).to(
self.device
),
prompt_token=prompt_speech_tokens,
prompt_token_len=torch.tensor(
[prompt_speech_tokens.shape[1]], dtype=torch.int32
).to(self.device),
prompt_feat=prompt_speech_feat,
prompt_feat_len=torch.tensor([prompt_speech_feat.shape[1]], dtype=torch.int32).to(self.device),
embedding=prompt_spk_embedding,
streaming=False,
finalize=True,
)
audio_hat, _ = self.token2wav_model.model.hift.inference(
speech_feat=tts_mel, cache_source=torch.zeros(1, 1, 0)
)
generated_wave = audio_hat.squeeze(0).cpu().numpy()
wav_tensor = pb_utils.Tensor.from_dlpack("waveform", to_dlpack(audio_hat))
inference_response = pb_utils.InferenceResponse(output_tensors=[wav_tensor])
responses.append(inference_response)
return responses