wenetspeech asr1 quant

examples/wenetspeech/asr1/local/quant.sh

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+#!/bin/bash
+
+if [ $# != 4 ];then
+    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+config_path=$1
+decode_config_path=$2
+ckpt_prefix=$3
+audio_file=$4
+
+mkdir -p data
+wget -nc https://paddlespeech.bj.bcebos.com/datasets/single_wav/zh/demo_01_03.wav -P data/
+if [ $? -ne 0 ]; then
+   exit 1
+fi
+
+if [ ! -f ${audio_file} ]; then
+    echo "Plase input the right audio_file path"
+    exit 1
+fi
+
+
+chunk_mode=false
+if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
+    chunk_mode=true
+fi
+
+# download language model
+#bash local/download_lm_ch.sh
+#if [ $? -ne 0 ]; then
+#    exit 1
+#fi
+
+for type in  attention_rescoring; do
+    echo "decoding ${type}"
+    batch_size=1
+    output_dir=${ckpt_prefix}
+    mkdir -p ${output_dir}
+    python3 -u ${BIN_DIR}/quant.py \
+    --ngpu ${ngpu} \
+    --config ${config_path} \
+    --decode_cfg ${decode_config_path} \
+    --result_file ${output_dir}/${type}.rsl \
+    --checkpoint_path ${ckpt_prefix} \
+    --opts decode.decoding_method ${type} \
+    --opts decode.decode_batch_size ${batch_size} \
+    --audio_file ${audio_file}
+
+    if [ $? -ne 0 ]; then
+        echo "Failed in evaluation!"
+        exit 1
+    fi
+done
+exit 0

paddlespeech/s2t/exps/u2/bin/quant.py

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+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+"""Evaluation for U2 model."""
+import os
+import sys
+from pathlib import Path
+
+import paddle
+import soundfile
+from yacs.config import CfgNode
+
+from paddlespeech.audio.transform.transformation import Transformation
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.models.u2 import U2Model
+from paddlespeech.s2t.training.cli import default_argument_parser
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.s2t.utils.utility import UpdateConfig
+from paddleslim import PTQ
+logger = Log(__name__).getlog()
+
+
+class U2Infer():
+    def __init__(self, config, args):
+        self.args = args
+        self.config = config
+        self.audio_file = args.audio_file
+
+        self.preprocess_conf = config.preprocess_config
+        self.preprocess_args = {"train": False}
+        self.preprocessing = Transformation(self.preprocess_conf)
+        self.reverse_weight = getattr(config.model_conf, 'reverse_weight', 0.0)
+        self.text_feature = TextFeaturizer(
+            unit_type=config.unit_type,
+            vocab=config.vocab_filepath,
+            spm_model_prefix=config.spm_model_prefix)
+
+        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
+
+        # model
+        model_conf = config
+        with UpdateConfig(model_conf):
+            model_conf.input_dim = config.feat_dim
+            model_conf.output_dim = self.text_feature.vocab_size
+        model = U2Model.from_config(model_conf)
+        self.model = model
+        self.model.eval()
+        self.ptq = PTQ()
+        self.model = self.ptq.quantize(model)
+
+        # load model
+        params_path = self.args.checkpoint_path + ".pdparams"
+        model_dict = paddle.load(params_path)
+        self.model.set_state_dict(model_dict)
+        logger.info(f"model_dict: {model_dict.keys()}")
+
+    def run(self):
+        check(args.audio_file)
+
+        with paddle.no_grad():
+            # read
+            audio, sample_rate = soundfile.read(
+                self.audio_file, dtype="int16", always_2d=True)
+            audio = audio[:, 0]
+            logger.info(f"audio shape: {audio.shape}")
+
+            # fbank
+            feat = self.preprocessing(audio, **self.preprocess_args)
+            logger.info(f"feat shape: {feat.shape}")
+
+            ilen = paddle.to_tensor(feat.shape[0])
+            xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(0)
+            decode_config = self.config.decode
+            logger.info(f"decode cfg: {decode_config}")
+            result_transcripts = self.model.decode(
+                xs,
+                ilen,
+                text_feature=self.text_feature,
+                decoding_method=decode_config.decoding_method,
+                beam_size=decode_config.beam_size,
+                ctc_weight=decode_config.ctc_weight,
+                decoding_chunk_size=decode_config.decoding_chunk_size,
+                num_decoding_left_chunks=decode_config.num_decoding_left_chunks,
+                simulate_streaming=decode_config.simulate_streaming,
+                reverse_weight=self.reverse_weight)
+            rsl = result_transcripts[0][0]
+            utt = Path(self.audio_file).name
+            logger.info(f"hyp: {utt} {result_transcripts[0][0]}")
+            # print(self.model)
+            # print(self.model.forward_encoder_chunk)
+            # return rsl
+            
+            logger.info("-------------start export ----------------------")
+            batch_size = 1
+            feat_dim = 80
+            model_size = 512
+            num_left_chunks = -1
+            logger.info(
+                f"U2 Export Model Params: batch_size {batch_size}, feat_dim {feat_dim}, model_size {model_size}, num_left_chunks {num_left_chunks}"
+            )
+
+            # ######################## self.model.forward_encoder_chunk ############
+            # input_spec = [
+            #     # (T,), int16
+            #     paddle.static.InputSpec(shape=[None], dtype='int16'),
+            # ]
+            # self.model.forward_feature = paddle.jit.to_static(
+            #     self.model.forward_feature, input_spec=input_spec)
+
+            ######################### self.model.forward_encoder_chunk ############
+            input_spec = [
+                # xs, (B, T, D)
+                paddle.static.InputSpec(
+                    shape=[batch_size, None, feat_dim], dtype='float32'),
+                # offset, int, but need be tensor
+                paddle.static.InputSpec(shape=[1], dtype='int32'),
+                # required_cache_size, int
+                num_left_chunks,
+                # att_cache
+                paddle.static.InputSpec(
+                    shape=[None, None, None, None], dtype='float32'),
+                # cnn_cache
+                paddle.static.InputSpec(
+                    shape=[None, None, None, None], dtype='float32')
+            ]
+            self.model.forward_encoder_chunk = paddle.jit.to_static(
+                self.model.forward_encoder_chunk, input_spec=input_spec)
+
+            ######################### self.model.ctc_activation ########################
+            input_spec = [
+                # encoder_out, (B,T,D)
+                paddle.static.InputSpec(
+                    shape=[batch_size, None, model_size], dtype='float32')
+            ]
+            self.model.ctc_activation = paddle.jit.to_static(
+                self.model.ctc_activation, input_spec=input_spec)
+
+            ######################### self.model.forward_attention_decoder ########################
+            reverse_weight = 0.3
+            input_spec = [
+                # hyps, (B, U)
+                paddle.static.InputSpec(shape=[None, None], dtype='int64'),
+                # hyps_lens, (B,)
+                paddle.static.InputSpec(shape=[None], dtype='int64'),
+                # encoder_out, (B,T,D)
+                paddle.static.InputSpec(
+                    shape=[batch_size, None, model_size], dtype='float32'),
+                reverse_weight
+            ]
+            self.model.forward_attention_decoder = paddle.jit.to_static(
+                self.model.forward_attention_decoder, input_spec=input_spec)
+            ################################################################################
+
+            # jit save
+            logger.info(f"export save: {self.args.export_path}")
+            config = {'is_static': True, 'combine_params':True, 'skip_forward':True}
+            self.ptq.save_quantized_model(self.model, self.args.export_path)
+            # paddle.jit.save(
+            #     self.model,
+            #     self.args.export_path,
+            #     combine_params=True,
+            #     skip_forward=True)
+
+    
+
+def check(audio_file):
+    if not os.path.isfile(audio_file):
+        print("Please input the right audio file path")
+        sys.exit(-1)
+
+    logger.info("checking the audio file format......")
+    try:
+        sig, sample_rate = soundfile.read(audio_file)
+    except Exception as e:
+        logger.error(str(e))
+        logger.error(
+            "can not open the wav file, please check the audio file format")
+        sys.exit(-1)
+    logger.info("The sample rate is %d" % sample_rate)
+    assert (sample_rate == 16000)
+    logger.info("The audio file format is right")
+
+
+def main(config, args):
+    U2Infer(config, args).run()
+
+
+if __name__ == "__main__":
+    parser = default_argument_parser()
+    # save asr result to
+    parser.add_argument(
+        "--result_file", type=str, help="path of save the asr result")
+    parser.add_argument(
+        "--audio_file", type=str, help="path of the input audio file")
+    parser.add_argument(
+        "--export_path", type=str, default='export', help="path of the input audio file")
+    args = parser.parse_args()
+
+    config = CfgNode(new_allowed=True)
+
+    if args.config:
+        config.merge_from_file(args.config)
+    if args.decode_cfg:
+        decode_confs = CfgNode(new_allowed=True)
+        decode_confs.merge_from_file(args.decode_cfg)
+        config.decode = decode_confs
+    if args.opts:
+        config.merge_from_list(args.opts)
+    config.freeze()
+    main(config, args)