diff --git a/examples/wenetspeech/asr1/local/quant.sh b/examples/wenetspeech/asr1/local/quant.sh index 9dfea9045..ac854aaad 100755 --- a/examples/wenetspeech/asr1/local/quant.sh +++ b/examples/wenetspeech/asr1/local/quant.sh @@ -1,7 +1,8 @@ #!/bin/bash +# ./local/quant.sh conf/chunk_conformer_u2pp.yaml conf/tuning/chunk_decode.yaml exp/chunk_conformer_u2pp/checkpoints/avg_10 data/wav.aishell.test.scp if [ $# != 4 ];then - echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_file" + echo "usage: ${0} config_path decode_config_path ckpt_path_prefix audio_scp" exit -1 fi @@ -11,16 +12,15 @@ echo "using $ngpu gpus..." config_path=$1 decode_config_path=$2 ckpt_prefix=$3 -audio_file=$4 +audio_scp=$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" +if [ ! -f ${audio_scp} ]; then + echo "Plase input the right audio_scp path" exit 1 fi @@ -49,7 +49,8 @@ for type in attention_rescoring; do --checkpoint_path ${ckpt_prefix} \ --opts decode.decoding_method ${type} \ --opts decode.decode_batch_size ${batch_size} \ - --audio_file ${audio_file} + --num_utts 200 \ + --audio_scp ${audio_scp} if [ $? -ne 0 ]; then echo "Failed in evaluation!" diff --git a/examples/wenetspeech/asr1/run.sh b/examples/wenetspeech/asr1/run.sh index ddce0a9c8..2ae7b31c6 100644 --- a/examples/wenetspeech/asr1/run.sh +++ b/examples/wenetspeech/asr1/run.sh @@ -54,3 +54,7 @@ if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then # test a single .wav file CUDA_VISIBLE_DEVICES=0 ./local/test_wav.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} ${audio_file} || exit -1 fi + +if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then + # export quant model, plesae see local/quant.sh +fi diff --git a/paddlespeech/s2t/exps/u2/bin/quant.py b/paddlespeech/s2t/exps/u2/bin/quant.py old mode 100644 new mode 100755 index c38134c57..6d361c5fd --- a/paddlespeech/s2t/exps/u2/bin/quant.py +++ b/paddlespeech/s2t/exps/u2/bin/quant.py @@ -11,13 +11,9 @@ # 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 - +"""Quantzation U2 model.""" import paddle -import soundfile +from kaldiio import ReadHelper from paddleslim import PTQ from yacs.config import CfgNode @@ -34,7 +30,7 @@ class U2Infer(): def __init__(self, config, args): self.args = args self.config = config - self.audio_file = args.audio_file + self.audio_scp = args.audio_scp self.preprocess_conf = config.preprocess_config self.preprocess_args = {"train": False} @@ -63,133 +59,112 @@ class U2Infer(): self.model.set_state_dict(model_dict) 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}") - reverse_weight = getattr(decode_config, 'reverse_weight', 0.0) - 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=reverse_weight) - rsl = result_transcripts[0][0] - utt = Path(self.audio_file).name - logger.info(f"hyp: {utt} {rsl}") - # print(self.model) - # print(self.model.forward_encoder_chunk) - - logger.info("-------------start quant ----------------------") - batch_size = 1 - feat_dim = 80 - model_size = 512 - num_left_chunks = -1 - reverse_weight = 0.3 - 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}, reverse_weight {reverse_weight}" - ) - - # ######################## 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 ######################## - 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") + cnt = 0 + with ReadHelper(f"scp:{self.audio_scp}") as reader: + for key, (rate, audio) in reader: + assert rate == 16000 + cnt += 1 + if cnt > args.num_utts: + break + + with paddle.no_grad(): + 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=decode_config.reverse_weight) + rsl = result_transcripts[0][0] + utt = key + logger.info(f"hyp: {utt} {rsl}") + # print(self.model) + # print(self.model.forward_encoder_chunk) + + logger.info("-------------start quant ----------------------") + batch_size = 1 + feat_dim = 80 + model_size = 512 + num_left_chunks = -1 + reverse_weight = 0.3 + 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}, reverse_weight {reverse_weight}" + ) + + # ######################## 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 ######################## + 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}") + self.ptq.ptq._convert(self.model) + paddle.jit.save( + self.model, + self.args.export_path, + combine_params=True, + skip_forward=True) def main(config, args): @@ -202,11 +177,16 @@ if __name__ == "__main__": 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") + "--audio_scp", type=str, help="path of the input audio file") + parser.add_argument( + "--num_utts", + type=int, + default=200, + help="num utts for quant calibrition.") parser.add_argument( "--export_path", type=str, - default='export', + default='export.jit.quant', help="path of the input audio file") args = parser.parse_args()