deepgeek/CosyVoice
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add flow cache inference code

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lyuxiang.lx
2025-04-07 21:23:09 +08:00
parent a69b7e275d
commit 39ffc50dec
4 changed files with 19 additions and 18 deletions
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2
README.md
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@@ -128,7 +128,7 @@ import torchaudio
**CosyVoice2 Usage**
```python
cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=False, load_trt=False, fp16=False)
cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=False, load_trt=False, fp16=False, use_flow_cache=False)
# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference
# zero_shot usage

6
cosyvoice/cli/cosyvoice.py
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@@ -129,7 +129,7 @@ class CosyVoice:
class CosyVoice2(CosyVoice):
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, use_flow_cache=False):
self.instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
self.fp16 = fp16
@@ -151,9 +151,9 @@ class CosyVoice2(CosyVoice):
if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
load_jit, load_trt, fp16 = False, False, False
logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16, use_flow_cache)
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir) if use_flow_cache is False else '{}/flow.cache.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'))

21
cosyvoice/cli/model.py
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@@ -288,19 +288,20 @@ class CosyVoice2Model(CosyVoiceModel):
llm: torch.nn.Module,
flow: torch.nn.Module,
hift: torch.nn.Module,
fp16: bool):
fp16: bool,
use_flow_cache: bool):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.llm = llm
self.flow = flow
self.hift = hift
self.fp16 = fp16
self.use_flow_cache = use_flow_cache
if self.fp16 is True:
self.llm.half()
self.flow.half()
self.token_hop_len = self.flow.encoder.static_chunk_size
# flow decoder required_cache_size
# TODO 基模型训练时没有设置num_decoding_left_chunks，需要重新训一下才能指定flow_decoder_required_cache_size
self.flow_decoder_required_cache_size = 999
# stream related params, check examples/libritts/cosyvoice2/conf/cosyvoice2.yaml
self.token_hop_len = 25
self.flow_decoder_required_cache_size = -1 if use_flow_cache is False else 1 * self.token_hop_len
# hift cache
self.mel_cache_len = 8
self.source_cache_len = int(self.mel_cache_len * 480)
@@ -339,7 +340,7 @@ class CosyVoice2Model(CosyVoiceModel):
return cache
def trim_flow_cache(self, cache):
if cache['decoder_cache']['down_blocks_kv_cache'].size(4) > self.flow_decoder_required_cache_size:
if self.flow_decoder_required_cache_size > 0:
cache['decoder_cache']['down_blocks_kv_cache'] = cache['decoder_cache']['down_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
cache['decoder_cache']['mid_blocks_kv_cache'] = cache['decoder_cache']['mid_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
cache['decoder_cache']['up_blocks_kv_cache'] = cache['decoder_cache']['up_blocks_kv_cache'][:, :, :, :, -self.flow_decoder_required_cache_size:]
@@ -399,10 +400,10 @@ class CosyVoice2Model(CosyVoiceModel):
prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
# this_uuid is used to track variables related to this inference thread
this_uuid = str(uuid.uuid1())
# NOTE flow model is only trained with static_chunk_size, so we need to trim flow prompt
n_chunk = int(flow_prompt_speech_token.size(1) / self.token_hop_len)
flow_prompt_speech_token = flow_prompt_speech_token[:, :n_chunk * self.token_hop_len]
prompt_speech_feat = prompt_speech_feat[:, :n_chunk * self.token_hop_len * 2]
# NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
if self.use_flow_cache is True:
flow_prompt_speech_token = flow_prompt_speech_token[:, -self.flow_decoder_required_cache_size:]
prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size * 2:]
with self.lock:
self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
self.hift_cache_dict[this_uuid] = None

8
examples/libritts/cosyvoice2/conf/cosyvoice2.yaml
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@@ -14,8 +14,8 @@ token_frame_rate: 25
token_mel_ratio: 2
# stream related params
chunk_size: 2 # streaming inference chunk size, in second
num_decoding_left_chunks: 1 # streaming inference flow decoder left chunk size
chunk_size: 25 # streaming inference chunk size, in token
num_decoding_left_chunks: 1 # streaming inference flow decoder left chunk size, <0 means use all left chunks
# model params
# for all class/function included in this repo, we use !<name> or !<new> for intialization, so that user may find all corresponding class/function according to one single yaml.
@@ -60,7 +60,7 @@ flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithXvec
input_size: 512
use_cnn_module: False
macaron_style: False
static_chunk_size: !ref <chunk_size> * <token_frame_rate>
static_chunk_size: !ref <chunk_size>
decoder: !new:cosyvoice.flow.flow_matching.CausalConditionalCFM
in_channels: 240
n_spks: 1
@@ -83,7 +83,7 @@ flow: !new:cosyvoice.flow.flow.CausalMaskedDiffWithXvec
num_mid_blocks: 12
num_heads: 8
act_fn: 'gelu'
static_chunk_size: !ref <chunk_size> * <token_frame_rate> * <token_mel_ratio> # here we use static_chunk_size because we want to fix kv cache size during inference
static_chunk_size: !ref <chunk_size> * <token_mel_ratio>
num_decoding_left_chunks: !ref <num_decoding_left_chunks>
hift: !new:cosyvoice.hifigan.generator.HiFTGenerator