387 lines
23 KiB
Python
387 lines
23 KiB
Python
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import Generator
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import torch
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import numpy as np
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import threading
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import time
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from torch.nn import functional as F
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from contextlib import nullcontext
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import uuid
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from cosyvoice.utils.common import fade_in_out
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from cosyvoice.utils.file_utils import convert_onnx_to_trt, export_cosyvoice2_vllm
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from cosyvoice.utils.common import TrtContextWrapper
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class CosyVoiceModel:
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def __init__(self,
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llm: torch.nn.Module,
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flow: torch.nn.Module,
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hift: torch.nn.Module,
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fp16: bool = False):
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.llm = llm
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self.flow = flow
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self.hift = hift
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self.fp16 = fp16
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if self.fp16 is True:
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self.llm.half()
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self.flow.half()
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self.token_min_hop_len = 2 * self.flow.input_frame_rate
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self.token_max_hop_len = 4 * self.flow.input_frame_rate
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self.token_overlap_len = 20
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# mel fade in out
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self.mel_overlap_len = int(self.token_overlap_len / self.flow.input_frame_rate * 22050 / 256)
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self.mel_window = np.hamming(2 * self.mel_overlap_len)
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# hift cache
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self.mel_cache_len = 20
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self.source_cache_len = int(self.mel_cache_len * 256)
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# speech fade in out
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self.speech_window = np.hamming(2 * self.source_cache_len)
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# rtf and decoding related
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self.stream_scale_factor = 1
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assert self.stream_scale_factor >= 1, 'stream_scale_factor should be greater than 1, change it according to your actual rtf'
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self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
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self.lock = threading.Lock()
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# dict used to store session related variable
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self.tts_speech_token_dict = {}
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self.llm_end_dict = {}
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self.mel_overlap_dict = {}
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self.flow_cache_dict = {}
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self.hift_cache_dict = {}
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def load(self, llm_model, flow_model, hift_model):
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self.llm.load_state_dict(torch.load(llm_model, map_location=self.device), strict=True)
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self.llm.to(self.device).eval()
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self.flow.load_state_dict(torch.load(flow_model, map_location=self.device), strict=True)
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self.flow.to(self.device).eval()
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# in case hift_model is a hifigan model
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hift_state_dict = {k.replace('generator.', ''): v for k, v in torch.load(hift_model, map_location=self.device).items()}
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self.hift.load_state_dict(hift_state_dict, strict=True)
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self.hift.to(self.device).eval()
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def load_jit(self, llm_text_encoder_model, llm_llm_model, flow_encoder_model):
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llm_text_encoder = torch.jit.load(llm_text_encoder_model, map_location=self.device)
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self.llm.text_encoder = llm_text_encoder
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llm_llm = torch.jit.load(llm_llm_model, map_location=self.device)
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self.llm.llm = llm_llm
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flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
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self.flow.encoder = flow_encoder
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def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, trt_concurrent, fp16):
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assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
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if not os.path.exists(flow_decoder_estimator_model) or os.path.getsize(flow_decoder_estimator_model) == 0:
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convert_onnx_to_trt(flow_decoder_estimator_model, self.get_trt_kwargs(), flow_decoder_onnx_model, fp16)
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del self.flow.decoder.estimator
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import tensorrt as trt
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with open(flow_decoder_estimator_model, 'rb') as f:
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estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
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assert estimator_engine is not None, 'failed to load trt {}'.format(flow_decoder_estimator_model)
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self.flow.decoder.estimator = TrtContextWrapper(estimator_engine, trt_concurrent=trt_concurrent, device=self.device)
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def get_trt_kwargs(self):
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min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2, 80, 4)]
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opt_shape = [(2, 80, 500), (2, 1, 500), (2, 80, 500), (2, 80, 500)]
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max_shape = [(2, 80, 3000), (2, 1, 3000), (2, 80, 3000), (2, 80, 3000)]
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input_names = ["x", "mask", "mu", "cond"]
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return {'min_shape': min_shape, 'opt_shape': opt_shape, 'max_shape': max_shape, 'input_names': input_names}
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def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
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with self.llm_context, torch.cuda.amp.autocast(self.fp16 is True and hasattr(self.llm, 'vllm') is False):
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if isinstance(text, Generator):
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assert isinstance(self, CosyVoice2Model) and not hasattr(self.llm, 'vllm'), 'streaming input text is only implemented for CosyVoice2 and do not support vllm!'
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for i in self.llm.inference_bistream(text=text,
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prompt_text=prompt_text.to(self.device),
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prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
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prompt_speech_token=llm_prompt_speech_token.to(self.device),
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prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
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embedding=llm_embedding.to(self.device)):
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self.tts_speech_token_dict[uuid].append(i)
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else:
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for i in self.llm.inference(text=text.to(self.device),
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text_len=torch.tensor([text.shape[1]], dtype=torch.int32).to(self.device),
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prompt_text=prompt_text.to(self.device),
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prompt_text_len=torch.tensor([prompt_text.shape[1]], dtype=torch.int32).to(self.device),
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prompt_speech_token=llm_prompt_speech_token.to(self.device),
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prompt_speech_token_len=torch.tensor([llm_prompt_speech_token.shape[1]], dtype=torch.int32).to(self.device),
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embedding=llm_embedding.to(self.device),
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uuid=uuid):
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self.tts_speech_token_dict[uuid].append(i)
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self.llm_end_dict[uuid] = True
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def vc_job(self, source_speech_token, uuid):
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self.tts_speech_token_dict[uuid] = source_speech_token.flatten().tolist()
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self.llm_end_dict[uuid] = True
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def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
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with torch.cuda.amp.autocast(self.fp16):
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tts_mel, self.flow_cache_dict[uuid] = self.flow.inference(token=token.to(self.device),
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token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_token=prompt_token.to(self.device),
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prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_feat=prompt_feat.to(self.device),
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prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
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embedding=embedding.to(self.device),
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flow_cache=self.flow_cache_dict[uuid])
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# mel overlap fade in out
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if self.mel_overlap_dict[uuid].shape[2] != 0:
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tts_mel = fade_in_out(tts_mel, self.mel_overlap_dict[uuid], self.mel_window)
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# append hift cache
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if self.hift_cache_dict[uuid] is not None:
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hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
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tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
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else:
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hift_cache_source = torch.zeros(1, 1, 0)
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# keep overlap mel and hift cache
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if finalize is False:
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self.mel_overlap_dict[uuid] = tts_mel[:, :, -self.mel_overlap_len:]
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tts_mel = tts_mel[:, :, :-self.mel_overlap_len]
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tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
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if self.hift_cache_dict[uuid] is not None:
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tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
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'source': tts_source[:, :, -self.source_cache_len:],
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'speech': tts_speech[:, -self.source_cache_len:]}
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tts_speech = tts_speech[:, :-self.source_cache_len]
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else:
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if speed != 1.0:
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assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
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tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
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tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
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if self.hift_cache_dict[uuid] is not None:
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tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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return tts_speech
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def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
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prompt_text=torch.zeros(1, 0, dtype=torch.int32),
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llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
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# this_uuid is used to track variables related to this inference thread
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this_uuid = str(uuid.uuid1())
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with self.lock:
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self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
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self.hift_cache_dict[this_uuid] = None
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self.mel_overlap_dict[this_uuid] = torch.zeros(1, 80, 0)
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self.flow_cache_dict[this_uuid] = torch.zeros(1, 80, 0, 2)
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if source_speech_token.shape[1] == 0:
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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else:
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p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
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p.start()
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if stream is True:
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token_hop_len = self.token_min_hop_len
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while True:
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time.sleep(0.1)
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if len(self.tts_speech_token_dict[this_uuid]) >= token_hop_len + self.token_overlap_len:
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_hop_len + self.token_overlap_len]) \
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.unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=False)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict[this_uuid] = self.tts_speech_token_dict[this_uuid][token_hop_len:]
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# increase token_hop_len for better speech quality
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token_hop_len = min(self.token_max_hop_len, int(token_hop_len * self.stream_scale_factor))
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if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) < token_hop_len + self.token_overlap_len:
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break
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p.join()
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# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=True)
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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p.join()
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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uuid=this_uuid,
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finalize=True,
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speed=speed)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict.pop(this_uuid)
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self.llm_end_dict.pop(this_uuid)
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self.mel_overlap_dict.pop(this_uuid)
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self.hift_cache_dict.pop(this_uuid)
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self.flow_cache_dict.pop(this_uuid)
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.current_stream().synchronize()
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class CosyVoice2Model(CosyVoiceModel):
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def __init__(self,
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llm: torch.nn.Module,
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flow: torch.nn.Module,
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hift: torch.nn.Module,
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fp16: bool = False):
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.llm = llm
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self.flow = flow
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self.hift = hift
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self.fp16 = fp16
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if self.fp16 is True:
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self.llm.half()
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self.flow.half()
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# NOTE must matching training static_chunk_size
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self.token_hop_len = 25
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# hift cache
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self.mel_cache_len = 8
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self.source_cache_len = int(self.mel_cache_len * 480)
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# speech fade in out
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self.speech_window = np.hamming(2 * self.source_cache_len)
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# rtf and decoding related
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self.llm_context = torch.cuda.stream(torch.cuda.Stream(self.device)) if torch.cuda.is_available() else nullcontext()
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self.lock = threading.Lock()
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# dict used to store session related variable
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self.tts_speech_token_dict = {}
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self.llm_end_dict = {}
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self.hift_cache_dict = {}
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def load_jit(self, flow_encoder_model):
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flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
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self.flow.encoder = flow_encoder
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def load_vllm(self, model_dir):
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export_cosyvoice2_vllm(self.llm, model_dir, self.device)
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from vllm import EngineArgs, LLMEngine
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engine_args = EngineArgs(model=model_dir,
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skip_tokenizer_init=True,
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enable_prompt_embeds=True,
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gpu_memory_utilization=0.2)
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self.llm.vllm = LLMEngine.from_engine_args(engine_args)
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self.llm.lock = threading.Lock()
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del self.llm.llm.model.model.layers
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def token2wav(self, token, prompt_token, prompt_feat, embedding, token_offset, uuid, stream=False, finalize=False, speed=1.0):
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with torch.cuda.amp.autocast(self.fp16):
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tts_mel, _ = self.flow.inference(token=token.to(self.device),
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token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_token=prompt_token.to(self.device),
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prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
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prompt_feat=prompt_feat.to(self.device),
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prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
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embedding=embedding.to(self.device),
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streaming=stream,
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finalize=finalize)
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tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
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# append hift cache
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if self.hift_cache_dict[uuid] is not None:
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hift_cache_mel, hift_cache_source = self.hift_cache_dict[uuid]['mel'], self.hift_cache_dict[uuid]['source']
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tts_mel = torch.concat([hift_cache_mel, tts_mel], dim=2)
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else:
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hift_cache_source = torch.zeros(1, 1, 0)
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# keep overlap mel and hift cache
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if finalize is False:
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tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
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if self.hift_cache_dict[uuid] is not None:
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tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
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'source': tts_source[:, :, -self.source_cache_len:],
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'speech': tts_speech[:, -self.source_cache_len:]}
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tts_speech = tts_speech[:, :-self.source_cache_len]
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else:
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if speed != 1.0:
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assert self.hift_cache_dict[uuid] is None, 'speed change only support non-stream inference mode'
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tts_mel = F.interpolate(tts_mel, size=int(tts_mel.shape[2] / speed), mode='linear')
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tts_speech, tts_source = self.hift.inference(speech_feat=tts_mel, cache_source=hift_cache_source)
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if self.hift_cache_dict[uuid] is not None:
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tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
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return tts_speech
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def tts(self, text=torch.zeros(1, 0, dtype=torch.int32), flow_embedding=torch.zeros(0, 192), llm_embedding=torch.zeros(0, 192),
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prompt_text=torch.zeros(1, 0, dtype=torch.int32),
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llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
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prompt_speech_feat=torch.zeros(1, 0, 80), source_speech_token=torch.zeros(1, 0, dtype=torch.int32), stream=False, speed=1.0, **kwargs):
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# this_uuid is used to track variables related to this inference thread
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this_uuid = str(uuid.uuid1())
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with self.lock:
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self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
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self.hift_cache_dict[this_uuid] = None
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if source_speech_token.shape[1] == 0:
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p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
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else:
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p = threading.Thread(target=self.vc_job, args=(source_speech_token, this_uuid))
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p.start()
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if stream is True:
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token_offset = 0
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prompt_token_pad = int(np.ceil(flow_prompt_speech_token.shape[1] / self.token_hop_len) * self.token_hop_len - flow_prompt_speech_token.shape[1])
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while True:
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time.sleep(0.1)
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this_token_hop_len = self.token_hop_len + prompt_token_pad if token_offset == 0 else self.token_hop_len
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if len(self.tts_speech_token_dict[this_uuid]) - token_offset >= this_token_hop_len + self.flow.pre_lookahead_len:
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid][:token_offset + this_token_hop_len + self.flow.pre_lookahead_len]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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token_offset=token_offset,
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uuid=this_uuid,
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stream=stream,
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finalize=False)
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token_offset += this_token_hop_len
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yield {'tts_speech': this_tts_speech.cpu()}
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if self.llm_end_dict[this_uuid] is True and len(self.tts_speech_token_dict[this_uuid]) - token_offset < this_token_hop_len + self.flow.pre_lookahead_len:
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break
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p.join()
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# deal with remain tokens, make sure inference remain token len equals token_hop_len when cache_speech is not None
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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token_offset=token_offset,
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uuid=this_uuid,
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finalize=True)
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yield {'tts_speech': this_tts_speech.cpu()}
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else:
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# deal with all tokens
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p.join()
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this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
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this_tts_speech = self.token2wav(token=this_tts_speech_token,
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prompt_token=flow_prompt_speech_token,
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prompt_feat=prompt_speech_feat,
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embedding=flow_embedding,
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token_offset=0,
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uuid=this_uuid,
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finalize=True,
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speed=speed)
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yield {'tts_speech': this_tts_speech.cpu()}
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with self.lock:
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self.tts_speech_token_dict.pop(this_uuid)
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self.llm_end_dict.pop(this_uuid)
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self.hift_cache_dict.pop(this_uuid)
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if torch.cuda.is_available():
|
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torch.cuda.empty_cache()
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torch.cuda.current_stream().synchronize()
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