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Update estimator count retrieval and memory pool limit in CosyVoice

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- Simplified estimator count retrieval in CosyVoice and CosyVoice2 classes to directly access the configs dictionary.
- Adjusted memory pool limit in the ONNX to TensorRT conversion function from 8GB to 1GB for optimized resource management.
This commit is contained in:
禾息
2025-04-16 14:39:06 +08:00
parent 7f4c9a2c64
commit 369f3c2c18
2 changed files with 3 additions and 3 deletions
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4
cosyvoice/cli/cosyvoice.py
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@@ -54,7 +54,7 @@ class CosyVoice:
'{}/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.estimator_count = configs['flow']['decoder']['estimator'].get('estimator_count', 1)
self.estimator_count = configs.get('estimator_count', 1)
self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
self.fp16, self.estimator_count)
@@ -180,7 +180,7 @@ class CosyVoice2(CosyVoice):
if load_jit:
self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
if load_trt:
self.estimator_count = configs['flow']['decoder']['estimator'].get('estimator_count', 1)
self.estimator_count = configs.get('estimator_count', 1)
self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
'{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
self.fp16, self.estimator_count)

2
cosyvoice/utils/file_utils.py
View File

@@ -61,7 +61,7 @@ def convert_onnx_to_trt(trt_model, onnx_model, fp16):
network = builder.create_network(network_flags)
parser = trt.OnnxParser(network, logger)
config = builder.create_builder_config()
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 33) # 8GB
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30) # 1GB
if fp16:
config.set_flag(trt.BuilderFlag.FP16)
profile = builder.create_optimization_profile()