Create utils.py

utils.py

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+# Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang)
+#               2024 Alibaba Inc (authors: Xiang Lyu, Zetao Hu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import torchaudio
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+logging.basicConfig(level=logging.DEBUG,
+                    format='%(asctime)s %(levelname)s %(message)s')
+
+
+def read_lists(list_file):
+    lists = []
+    with open(list_file, 'r', encoding='utf8') as fin:
+        for line in fin:
+            lists.append(line.strip())
+    return lists
+
+
+def read_json_lists(list_file):
+    lists = read_lists(list_file)
+    results = {}
+    for fn in lists:
+        with open(fn, 'r', encoding='utf8') as fin:
+            results.update(json.load(fin))
+    return results
+
+
+def load_wav(wav, target_sr):
+    speech, sample_rate = torchaudio.load(wav, backend='soundfile')
+    speech = speech.mean(dim=0, keepdim=True)
+    if sample_rate != target_sr:
+        assert sample_rate > target_sr, 'wav sample rate {} must be greater than {}'.format(sample_rate, target_sr)
+        speech = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sr)(speech)
+    return speech
+
+
+def convert_onnx_to_trt(trt_model, onnx_model, fp16):
+    import tensorrt as trt
+    _min_shape = [(2, 80, 4), (2, 1, 4), (2, 80, 4), (2,), (2, 80), (2, 80, 4)]
+    _opt_shape = [(2, 80, 193), (2, 1, 193), (2, 80, 193), (2,), (2, 80), (2, 80, 193)]
+    _max_shape = [(2, 80, 6800), (2, 1, 6800), (2, 80, 6800), (2,), (2, 80), (2, 80, 6800)]
+    input_names = ["x", "mask", "mu", "t", "spks", "cond"]
+
+    logging.info("Converting onnx to trt...")
+    network_flags = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
+    logger = trt.Logger(trt.Logger.INFO)
+    builder = trt.Builder(logger)
+    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 << 30)  # 1GB
+    if fp16:
+        config.set_flag(trt.BuilderFlag.FP16)
+    profile = builder.create_optimization_profile()
+    # load onnx model
+    with open(onnx_model, "rb") as f:
+        if not parser.parse(f.read()):
+            for error in range(parser.num_errors):
+                print(parser.get_error(error))
+            raise ValueError('failed to parse {}'.format(onnx_model))
+    # set input shapes
+    for i in range(len(input_names)):
+        profile.set_shape(input_names[i], _min_shape[i], _opt_shape[i], _max_shape[i])
+    tensor_dtype = trt.DataType.HALF if fp16 else trt.DataType.FLOAT
+    # set input and output data type
+    for i in range(network.num_inputs):
+        input_tensor = network.get_input(i)
+        input_tensor.dtype = tensor_dtype
+    for i in range(network.num_outputs):
+        output_tensor = network.get_output(i)
+        output_tensor.dtype = tensor_dtype
+    config.add_optimization_profile(profile)
+    engine_bytes = builder.build_serialized_network(network, config)
+    # save trt engine
+    with open(trt_model, "wb") as f:
+        f.write(engine_bytes)