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Configuration error
Configuration error
禾息
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18599be
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Parent(s):
2940836
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Browse files- cosyvoice/bin/export_trt.py +14 -0
- cosyvoice/cli/model.py +1 -5
- cosyvoice/flow/flow_matching.py +0 -1
cosyvoice/bin/export_trt.py
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@@ -1,3 +1,17 @@
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import argparse
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import logging
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import os
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# Copyright (c) 2024 Antgroup Inc (authors: Zhoubofan, [email protected])
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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 argparse
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import logging
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import os
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cosyvoice/cli/model.py
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@@ -20,11 +20,6 @@ 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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try:
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import tensorrt as trt
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except ImportError:
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...
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class CosyVoiceModel:
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def __init__(self,
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@@ -72,6 +67,7 @@ class CosyVoiceModel:
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self.llm.llm = llm_llm
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def load_trt(self, model_dir, use_fp16):
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trt_file_name = 'estimator_fp16.plan' if use_fp16 else 'estimator_fp32.plan'
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trt_file_path = os.path.join(model_dir, trt_file_name)
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if not os.path.isfile(trt_file_path):
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import uuid
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from cosyvoice.utils.common import fade_in_out
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class CosyVoiceModel:
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def __init__(self,
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self.llm.llm = llm_llm
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def load_trt(self, model_dir, use_fp16):
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import tensorrt as trt
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trt_file_name = 'estimator_fp16.plan' if use_fp16 else 'estimator_fp32.plan'
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trt_file_path = os.path.join(model_dir, trt_file_name)
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if not os.path.isfile(trt_file_path):
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cosyvoice/flow/flow_matching.py
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@@ -102,7 +102,6 @@ class ConditionalCFM(BASECFM):
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if self.estimator is not None:
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return self.estimator.forward(x, mask, mu, t, spks, cond)
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else:
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print("-----------")
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assert self.training is False, 'tensorrt cannot be used in training'
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bs = x.shape[0]
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hs = x.shape[1]
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if self.estimator is not None:
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return self.estimator.forward(x, mask, mu, t, spks, cond)
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else:
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assert self.training is False, 'tensorrt cannot be used in training'
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bs = x.shape[0]
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hs = x.shape[1]
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