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Merge pull request #849 from FunAudioLLM/dev/lyuxiang.lx

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update gradio
This commit is contained in:
Xiang Lyu
2025-01-08 11:12:31 +08:00
committed by GitHub
parent 5d12ced727 1e52c6071e
commit ac75ae5184
2 changed files with 18 additions and 15 deletions
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31
cosyvoice/flow/flow_matching.py
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@@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import threading
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from matcha.models.components.flow_matching import BASECFM from matcha.models.components.flow_matching import BASECFM
@@ -30,6 +31,7 @@ class ConditionalCFM(BASECFM):
in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0) in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
# Just change the architecture of the estimator here # Just change the architecture of the estimator here
self.estimator = estimator self.estimator = estimator
self.lock = threading.Lock()
@torch.inference_mode() @torch.inference_mode()
def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)): def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
@@ -123,20 +125,21 @@ class ConditionalCFM(BASECFM):
if isinstance(self.estimator, torch.nn.Module): if isinstance(self.estimator, torch.nn.Module):
return self.estimator.forward(x, mask, mu, t, spks, cond) return self.estimator.forward(x, mask, mu, t, spks, cond)
else: else:
self.estimator.set_input_shape('x', (2, 80, x.size(2))) with self.lock:
self.estimator.set_input_shape('mask', (2, 1, x.size(2))) self.estimator.set_input_shape('x', (2, 80, x.size(2)))
self.estimator.set_input_shape('mu', (2, 80, x.size(2))) self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
self.estimator.set_input_shape('t', (2,)) self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
self.estimator.set_input_shape('spks', (2, 80)) self.estimator.set_input_shape('t', (2,))
self.estimator.set_input_shape('cond', (2, 80, x.size(2))) self.estimator.set_input_shape('spks', (2, 80))
# run trt engine self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
self.estimator.execute_v2([x.contiguous().data_ptr(), # run trt engine
mask.contiguous().data_ptr(), self.estimator.execute_v2([x.contiguous().data_ptr(),
mu.contiguous().data_ptr(), mask.contiguous().data_ptr(),
t.contiguous().data_ptr(), mu.contiguous().data_ptr(),
spks.contiguous().data_ptr(), t.contiguous().data_ptr(),
cond.contiguous().data_ptr(), spks.contiguous().data_ptr(),
x.data_ptr()]) cond.contiguous().data_ptr(),
x.data_ptr()])
return x return x
def compute_loss(self, x1, mask, mu, spks=None, cond=None): def compute_loss(self, x1, mask, mu, spks=None, cond=None):

2
requirements.txt
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@@ -4,7 +4,7 @@ conformer==0.3.2
deepspeed==0.14.2; sys_platform == 'linux' deepspeed==0.14.2; sys_platform == 'linux'
diffusers==0.27.2 diffusers==0.27.2
gdown==5.1.0 gdown==5.1.0
gradio==4.32.2 gradio==5.4.0
grpcio==1.57.0 grpcio==1.57.0
grpcio-tools==1.57.0 grpcio-tools==1.57.0
huggingface-hub==0.25.2 huggingface-hub==0.25.2