TianYuan
3 years ago
parent
3c8f30c7a4
commit
7aecb2c4bb
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train_output_path=$1
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stage=1
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stop_stage=1
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# only support default_fastspeech2 + hifigan now!
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# synthesize from metadata
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../ort_predict.py \
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--inference_dir=${train_output_path}/inference_onnx \
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--am=fastspeech2_csmsc \
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--voc=hifigan_csmsc \
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--test_metadata=dump/test/norm/metadata.jsonl \
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--output_dir=${train_output_path}/onnx_infer_out \
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--device=cpu \
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--cpu_threads=2
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fi
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# e2e, synthesize from text
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../ort_predict_e2e.py \
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--inference_dir=${train_output_path}/inference_onnx \
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--am=fastspeech2_csmsc \
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--voc=hifigan_csmsc \
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--output_dir=${train_output_path}/onnx_infer_out_e2e \
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--text=${BIN_DIR}/../csmsc_test.txt \
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--phones_dict=dump/phone_id_map.txt \
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--device=cpu \
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--cpu_threads=2
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fi
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@ -0,0 +1,158 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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from pathlib import Path
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import jsonlines
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import numpy as np
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import onnxruntime as ort
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import get_test_dataset
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from paddlespeech.t2s.utils import str2bool
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def get_sess(args, filed='am'):
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full_name = ''
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if filed == 'am':
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full_name = args.am
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elif filed == 'voc':
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full_name = args.voc
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model_dir = str(Path(args.inference_dir) / (full_name + ".onnx"))
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sess_options = ort.SessionOptions()
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sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
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if args.device == "gpu":
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# fastspeech2 can't use trt now!
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if args.use_trt:
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providers = ['TensorrtExecutionProvider']
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else:
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providers = ['CUDAExecutionProvider']
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elif args.device == "cpu":
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providers = ['CPUExecutionProvider']
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sess_options.intra_op_num_threads = args.cpu_threads
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sess = ort.InferenceSession(
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model_dir, providers=providers, sess_options=sess_options)
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return sess
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def ort_predict(args):
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# construct dataset for evaluation
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with jsonlines.open(args.test_metadata, 'r') as reader:
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test_metadata = list(reader)
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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test_dataset = get_test_dataset(args, test_metadata, am_name, am_dataset)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# am
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am_sess = get_sess(args, filed='am')
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# vocoder
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voc_sess = get_sess(args, filed='voc')
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# am warmup
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for batch in [27, 38, 54]:
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data = np.random.randint(1, 266, size=(batch, ))
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am_sess.run(None, {"text": data})
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# voc warmup
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for batch in [227, 308, 544]:
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data = np.random.rand(batch, 80).astype("float32")
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voc_sess.run(None, {"logmel": data})
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print("warm up done!")
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N = 0
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T = 0
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for example in test_dataset:
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utt_id = example['utt_id']
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phone_ids = example["text"]
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with timer() as t:
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mel = am_sess.run(output_names=None, input_feed={'text': phone_ids})
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mel = mel[0]
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wav = voc_sess.run(output_names=None, input_feed={'logmel': mel})
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N += len(wav[0])
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T += t.elapse
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speed = len(wav[0]) / t.elapse
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rtf = fs / speed
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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np.array(wav)[0],
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samplerate=fs)
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
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def parse_args():
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parser = argparse.ArgumentParser(description="Infernce with onnxruntime.")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'fastspeech2_csmsc',
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],
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help='Choose acoustic model type of tts task.')
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# voc
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parser.add_argument(
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'--voc',
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type=str,
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default='hifigan_csmsc',
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choices=[
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'hifigan_csmsc', 'mb_melgan_csmsc'
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],
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help='Choose vocoder type of tts task.')
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# other
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parser.add_argument(
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"--inference_dir", type=str, help="dir to save inference models")
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parser.add_argument("--test_metadata", type=str, help="test metadata.")
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parser.add_argument("--output_dir", type=str, help="output dir")
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# inference
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parser.add_argument(
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"--use_trt",
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type=str2bool,
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default=False,
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help="Whether to use inference engin TensorRT.", )
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parser.add_argument(
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"--device",
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default="gpu",
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choices=["gpu", "cpu"],
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help="Device selected for inference.", )
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parser.add_argument('--cpu_threads', type=int, default=1)
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args, _ = parser.parse_known_args()
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return args
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def main():
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args = parse_args()
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ort_predict(args)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,178 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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from pathlib import Path
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import numpy as np
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import onnxruntime as ort
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.utils import str2bool
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def get_sess(args, filed='am'):
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full_name = ''
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if filed == 'am':
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full_name = args.am
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elif filed == 'voc':
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full_name = args.voc
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model_dir = str(Path(args.inference_dir) / (full_name + ".onnx"))
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sess_options = ort.SessionOptions()
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sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
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sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
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if args.device == "gpu":
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# fastspeech2 can't use trt now!
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if args.use_trt:
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providers = ['TensorrtExecutionProvider']
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else:
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providers = ['CUDAExecutionProvider']
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elif args.device == "cpu":
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providers = ['CPUExecutionProvider']
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sess_options.intra_op_num_threads = args.cpu_threads
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sess = ort.InferenceSession(
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model_dir, providers=providers, sess_options=sess_options)
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return sess
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def ort_predict(args):
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# frontend
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frontend = get_frontend(args)
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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sentences = get_sentences(args)
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am_name = args.am[:args.am.rindex('_')]
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am_dataset = args.am[args.am.rindex('_') + 1:]
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# am
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am_sess = get_sess(args, filed='am')
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# vocoder
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voc_sess = get_sess(args, filed='voc')
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# am warmup
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for batch in [27, 38, 54]:
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data = np.random.randint(1, 266, size=(batch, ))
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am_sess.run(None, {"text": data})
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# voc warmup
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for batch in [227, 308, 544]:
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data = np.random.rand(batch, 80).astype("float32")
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voc_sess.run(None, {"logmel": data})
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print("warm up done!")
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N = 0
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T = 0
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merge_sentences = True
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for utt_id, sentence in sentences:
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with timer() as t:
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if args.lang == 'zh':
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input_ids = frontend.get_input_ids(
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sentence, merge_sentences=merge_sentences)
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phone_ids = input_ids["phone_ids"]
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else:
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print("lang should in be 'zh' here!")
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# merge_sentences=True here, so we only use the first item of phone_ids
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phone_ids = phone_ids[0].numpy()
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mel = am_sess.run(output_names=None, input_feed={'text': phone_ids})
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mel = mel[0]
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wav = voc_sess.run(output_names=None, input_feed={'logmel': mel})
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N += len(wav[0])
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T += t.elapse
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speed = len(wav[0]) / t.elapse
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rtf = fs / speed
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sf.write(
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str(output_dir / (utt_id + ".wav")),
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np.array(wav)[0],
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samplerate=fs)
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {len(wav[0])}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
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def parse_args():
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parser = argparse.ArgumentParser(description="Infernce with onnxruntime.")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'fastspeech2_csmsc',
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],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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# voc
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parser.add_argument(
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'--voc',
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type=str,
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default='hifigan_csmsc',
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choices=[
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'hifigan_csmsc', 'mb_melgan_csmsc'
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],
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help='Choose vocoder type of tts task.')
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# other
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parser.add_argument(
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"--inference_dir", type=str, help="dir to save inference models")
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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parser.add_argument("--output_dir", type=str, help="output dir")
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parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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# inference
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parser.add_argument(
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"--use_trt",
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type=str2bool,
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default=False,
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help="Whether to use inference engin TensorRT.", )
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parser.add_argument(
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"--device",
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default="gpu",
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choices=["gpu", "cpu"],
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help="Device selected for inference.", )
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parser.add_argument('--cpu_threads', type=int, default=1)
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args, _ = parser.parse_known_args()
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return args
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def main():
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args = parse_args()
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ort_predict(args)
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if __name__ == "__main__":
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main()
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