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181 lines
7.0 KiB
181 lines
7.0 KiB
# Copyright (c) 2021 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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"""Quantzation U2 model."""
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import paddle
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from kaldiio import ReadHelper
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from paddleslim import PTQ
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from paddlespeech.audio.transform.transformation import Transformation
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.models.u2 import U2Model
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from paddlespeech.s2t.training.cli import config_from_args
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.s2t.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class U2Infer():
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def __init__(self, config, args):
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self.args = args
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self.config = config
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self.audio_scp = args.audio_scp
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self.preprocess_conf = config.preprocess_config
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self.preprocess_args = {"train": False}
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self.preprocessing = Transformation(self.preprocess_conf)
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self.text_feature = TextFeaturizer(
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unit_type=config.unit_type,
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vocab=config.vocab_filepath,
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spm_model_prefix=config.spm_model_prefix)
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paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
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# model
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model_conf = config
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with UpdateConfig(model_conf):
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model_conf.input_dim = config.feat_dim
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model_conf.output_dim = self.text_feature.vocab_size
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model = U2Model.from_config(model_conf)
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self.model = model
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self.model.eval()
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self.ptq = PTQ()
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self.model = self.ptq.quantize(model)
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# load model
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params_path = self.args.checkpoint_path + ".pdparams"
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model_dict = paddle.load(params_path)
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self.model.set_state_dict(model_dict)
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def run(self):
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cnt = 0
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with ReadHelper(f"scp:{self.audio_scp}") as reader:
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for key, (rate, audio) in reader:
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assert rate == 16000
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cnt += 1
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if cnt > args.num_utts:
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break
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with paddle.no_grad():
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logger.info(f"audio shape: {audio.shape}")
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# fbank
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feat = self.preprocessing(audio, **self.preprocess_args)
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logger.info(f"feat shape: {feat.shape}")
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ilen = paddle.to_tensor(feat.shape[0])
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xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(0)
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decode_config = self.config.decode
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logger.info(f"decode cfg: {decode_config}")
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result_transcripts = self.model.decode(
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xs,
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ilen,
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text_feature=self.text_feature,
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decoding_method=decode_config.decoding_method,
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beam_size=decode_config.beam_size,
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ctc_weight=decode_config.ctc_weight,
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decoding_chunk_size=decode_config.decoding_chunk_size,
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num_decoding_left_chunks=decode_config.
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num_decoding_left_chunks,
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simulate_streaming=decode_config.simulate_streaming,
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reverse_weight=decode_config.reverse_weight)
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rsl = result_transcripts[0][0]
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utt = key
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logger.info(f"hyp: {utt} {rsl}")
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# print(self.model)
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# print(self.model.forward_encoder_chunk)
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logger.info("-------------start quant ----------------------")
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batch_size = 1
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feat_dim = 80
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model_size = 512
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num_left_chunks = -1
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reverse_weight = 0.3
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logger.info(
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f"U2 Export Model Params: batch_size {batch_size}, feat_dim {feat_dim}, model_size {model_size}, num_left_chunks {num_left_chunks}, reverse_weight {reverse_weight}"
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)
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# ######################## self.model.forward_encoder_chunk ############
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# input_spec = [
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# # (T,), int16
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# paddle.static.InputSpec(shape=[None], dtype='int16'),
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# ]
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# self.model.forward_feature = paddle.jit.to_static(
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# self.model.forward_feature, input_spec=input_spec)
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######################### self.model.forward_encoder_chunk ############
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input_spec = [
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# xs, (B, T, D)
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paddle.static.InputSpec(
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shape=[batch_size, None, feat_dim], dtype='float32'),
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# offset, int, but need be tensor
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paddle.static.InputSpec(shape=[1], dtype='int32'),
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# required_cache_size, int
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num_left_chunks,
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# att_cache
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paddle.static.InputSpec(
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shape=[None, None, None, None], dtype='float32'),
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# cnn_cache
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paddle.static.InputSpec(
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shape=[None, None, None, None], dtype='float32')
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]
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self.model.forward_encoder_chunk = paddle.jit.to_static(
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self.model.forward_encoder_chunk, input_spec=input_spec)
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######################### self.model.ctc_activation ########################
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input_spec = [
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# encoder_out, (B,T,D)
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paddle.static.InputSpec(
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shape=[batch_size, None, model_size], dtype='float32')
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]
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self.model.ctc_activation = paddle.jit.to_static(
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self.model.ctc_activation, input_spec=input_spec)
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######################### self.model.forward_attention_decoder ########################
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input_spec = [
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# hyps, (B, U)
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paddle.static.InputSpec(shape=[None, None], dtype='int64'),
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# hyps_lens, (B,)
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paddle.static.InputSpec(shape=[None], dtype='int64'),
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# encoder_out, (B,T,D)
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paddle.static.InputSpec(
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shape=[batch_size, None, model_size], dtype='float32'),
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reverse_weight
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]
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self.model.forward_attention_decoder = paddle.jit.to_static(
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self.model.forward_attention_decoder, input_spec=input_spec)
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################################################################################
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# jit save
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logger.info(f"export save: {self.args.export_path}")
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self.ptq.ptq._convert(self.model)
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paddle.jit.save(
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self.model,
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self.args.export_path,
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combine_params=True,
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skip_forward=True)
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def main(config, args):
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U2Infer(config, args).run()
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if __name__ == "__main__":
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parser = default_argument_parser()
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args = parser.parse_args()
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config = config_from_args(args)
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main(config, args)
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