deepgeek/CosyVoice
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lyuxiang.lx
2024-12-31 17:08:11 +08:00
parent 2745d47e92
commit 77d8cf13a3
11 changed files with 163 additions and 158 deletions
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42
cosyvoice/cli/cosyvoice.py
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@@ -25,14 +25,15 @@ from cosyvoice.utils.class_utils import get_model_type
class CosyVoice:
def __init__(self, model_dir, load_jit=True, load_onnx=False, fp16=True):
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
self.instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
self.fp16 = fp16
if not os.path.exists(model_dir):
model_dir = snapshot_download(model_dir)
with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
configs = load_hyperpyyaml(f)
assert get_model_type(configs) == CosyVoiceModel, 'do not use {} for CosyVoice initialization!'.format(model_dir)
assert get_model_type(configs) != CosyVoice2Model, 'do not use {} for CosyVoice initialization!'.format(model_dir)
self.frontend = CosyVoiceFrontEnd(configs['get_tokenizer'],
configs['feat_extractor'],
'{}/campplus.onnx'.format(model_dir),
@@ -40,20 +41,19 @@ class CosyVoice:
'{}/spk2info.pt'.format(model_dir),
configs['allowed_special'])
self.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and (fp16 is True or load_jit is True):
load_jit = False
fp16 = False
logging.warning('cpu do not support fp16 and jit, force set to False')
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 = CosyVoiceModel(configs['llm'], configs['flow'], configs['hift'], fp16)
self.model.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/llm.text_encoder.fp16.zip'.format(model_dir),
'{}/llm.llm.fp16.zip'.format(model_dir),
'{}/flow.encoder.fp32.zip'.format(model_dir))
if load_onnx:
self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir))
self.model.load_jit('{}/llm.text_encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
if load_trt:
self.model.load_trt('{}/flow.decoder.estimator.{}.v100.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
del configs
def list_available_spks(self):
@@ -123,9 +123,10 @@ class CosyVoice:
class CosyVoice2(CosyVoice):
def __init__(self, model_dir, load_jit=False, load_onnx=False, load_trt=False):
def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
self.instruct = True if '-Instruct' in model_dir else False
self.model_dir = model_dir
self.fp16 = fp16
if not os.path.exists(model_dir):
model_dir = snapshot_download(model_dir)
with open('{}/cosyvoice.yaml'.format(model_dir), 'r') as f:
@@ -138,22 +139,17 @@ class CosyVoice2(CosyVoice):
'{}/spk2info.pt'.format(model_dir),
configs['allowed_special'])
self.sample_rate = configs['sample_rate']
if torch.cuda.is_available() is False and load_jit is True:
load_jit = False
logging.warning('cpu do not support jit, force set to False')
self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'])
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.load('{}/llm.pt'.format(model_dir),
'{}/flow.pt'.format(model_dir),
'{}/hift.pt'.format(model_dir))
if load_jit:
self.model.load_jit('{}/flow.encoder.fp32.zip'.format(model_dir))
if load_trt is True and load_onnx is True:
load_onnx = False
logging.warning('can not set both load_trt and load_onnx to True, force set load_onnx to False')
if load_onnx:
self.model.load_onnx('{}/flow.decoder.estimator.fp32.onnx'.format(model_dir))
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.fp16.Volta.plan'.format(model_dir))
self.model.load_trt('{}/flow.decoder.estimator.{}.v100.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
del configs
def inference_instruct(self, *args, **kwargs):