format wav2vec2 demo

examples/librispeech/asr3/conf/preprocess.yaml

 process:
-  # extract kaldi fbank from PCM
+    # use raw audio
   - type: wav_process
-    dither: 0.1
+    dither: 0.0

examples/librispeech/asr3/conf/tuning/decode.yaml

 decode_batch_size: 1
 error_rate_type: wer
-decoding_method: ctc_greedy_search  # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+decoding_method: ctc_greedy_search  # 'ctc_greedy_search', 'ctc_prefix_beam_search'
 beam_size: 10
-ctc_weight: 0.5 # ctc weight for attention rescoring decode mode.
-decoding_chunk_size: -1 # decoding chunk size. Defaults to -1.
-    # <0: for decoding, use full chunk.
-    # >0: for decoding, use fixed chunk size as set.
-    # 0: used for training, it's prohibited here. 
-num_decoding_left_chunks: -1  # number of left chunks for decoding. Defaults to -1.
-simulate_streaming: False  # simulate streaming inference. Defaults to False.

examples/librispeech/asr3/run.sh

@@ -36,9 +36,8 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
     avg.sh best exp/${ckpt}/checkpoints ${avg_num}
 fi
 
-
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
-    # attetion resocre decoder
+    # greedy search decoder
     CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 

paddlespeech/audio/transform/spectrogram.py

@@ -383,7 +383,7 @@ class LogMelSpectrogramKaldi():
 
 
 class WavProcess():
-    def __init__(self, dither=0.1):
+    def __init__(self, dither=0.0):
         """
         Args:
             dither (float): Dithering constant

paddlespeech/s2t/exps/wav2vec2/bin/test.py

@@ -20,8 +20,6 @@ from paddlespeech.s2t.exps.wav2vec2.model import Wav2Vec2ASRTester as Tester
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils.utility import print_arguments
 
-# TODO(hui zhang): dynamic load
-
 
 def main_sp(config, args):
     exp = Tester(config, args)

paddlespeech/s2t/exps/wav2vec2/model.py

@@ -25,9 +25,7 @@ import paddle
 from paddle import distributed as dist
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
-from paddlespeech.s2t.io.dataloader import BatchDataLoader
 from paddlespeech.s2t.io.dataloader import DataLoaderFactory
-from paddlespeech.s2t.io.dataloader import StreamDataLoader
 from paddlespeech.s2t.models.wav2vec2.processing.speech_augmentation import TimeDomainSpecAugment
 from paddlespeech.s2t.models.wav2vec2.wav2vec2_ASR import Wav2vec2ASR
 from paddlespeech.s2t.training.optimizer import OptimizerFactory
@@ -300,7 +298,6 @@ class Wav2Vec2ASRTrainer(Trainer):
                 "epsilon": optim_conf.epsilon,
                 "rho": optim_conf.rho,
                 "parameters": parameters,
-                "epsilon": 1e-9 if optim_type == 'noam' else None,
                 "beta1": 0.9 if optim_type == 'noam' else None,
                 "beat2": 0.98 if optim_type == 'noam' else None,
             }

paddlespeech/s2t/models/wav2vec2/wav2vec2_ASR.py

@@ -39,7 +39,7 @@ class Wav2vec2ASR(nn.Layer):
                        enc_n_units=config.dnn_neurons,
                        blank_id=config.blank_id,
                        dropout_rate=config.ctc_dropout_rate,
-                       reduction=True)
+                       reduction='mean')
 
     def forward(self, wav, wavs_lens_rate, target, target_lens_rate):
         if self.normalize_wav:

paddlespeech/s2t/modules/ctc.py

@@ -53,7 +53,7 @@ class CTCDecoderBase(nn.Layer):
                  enc_n_units,
                  blank_id=0,
                  dropout_rate: float=0.0,
-                 reduction: bool=True,
+                 reduction: Union[str, bool]=True,
                  batch_average: bool=True,
                  grad_norm_type: Union[str, None]=None):
         """CTC decoder
@@ -73,7 +73,10 @@ class CTCDecoderBase(nn.Layer):
         self.odim = odim
         self.dropout = nn.Dropout(dropout_rate)
         self.ctc_lo = Linear(enc_n_units, self.odim)
-        reduction_type = "sum" if reduction else "none"
+        if isinstance(reduction, bool):
+            reduction_type = "sum" if reduction else "none"
+        else:
+            reduction_type = reduction
         self.criterion = CTCLoss(
             blank=self.blank_id,
             reduction=reduction_type,