aishell support utt2spk

examples/aishell/s1/conf/transformer.yaml

+# https://yaml.org/type/float.html
+data:
+  train_manifest: data/manifest.train
+  dev_manifest: data/manifest.dev
+  test_manifest: data/manifest.test
+  min_input_len: 0.5
+  max_input_len: 20.0 # second
+  min_output_len: 0.0
+  max_output_len: 400.0
+  min_output_input_ratio: 0.05
+  max_output_input_ratio: 10.0
+
+
+collator:
+  vocab_filepath: data/vocab.txt 
+  unit_type: 'char'
+  spm_model_prefix: ''
+  augmentation_config: conf/preprocess.yaml
+  batch_size: 64
+  raw_wav: True  # use raw_wav or kaldi feature
+  spectrum_type: fbank #linear, mfcc, fbank
+  feat_dim: 80
+  delta_delta: False
+  dither: 1.0
+  target_sample_rate: 16000
+  max_freq: None
+  n_fft: None
+  stride_ms: 10.0
+  window_ms: 25.0
+  use_dB_normalization: True 
+  target_dB: -20
+  random_seed: 0
+  keep_transcription_text: False
+  sortagrad: True 
+  shuffle_method: batch_shuffle
+  num_workers: 2
+
+# network architecture
+model:
+    cmvn_file: 
+    cmvn_file_type: "json"
+    # encoder related
+    encoder: transformer
+    encoder_conf:
+        output_size: 256    # dimension of attention
+        attention_heads: 4
+        linear_units: 2048  # the number of units of position-wise feed forward
+        num_blocks: 12      # the number of encoder blocks
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        attention_dropout_rate: 0.0
+        input_layer: conv2d # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+        normalize_before: true
+
+    # decoder related
+    decoder: transformer
+    decoder_conf:
+        attention_heads: 4
+        linear_units: 2048
+        num_blocks: 6
+        dropout_rate: 0.1
+        positional_dropout_rate: 0.1
+        self_attention_dropout_rate: 0.0
+        src_attention_dropout_rate: 0.0
+
+    # hybrid CTC/attention
+    model_conf:
+        ctc_weight: 0.3
+        ctc_dropoutrate: 0.0
+        ctc_grad_norm_type: null 
+        lsm_weight: 0.1     # label smoothing option
+        length_normalized_loss: false
+
+
+training:
+  n_epoch: 240 
+  accum_grad: 2
+  global_grad_clip: 5.0
+  optim: adam
+  optim_conf:
+    lr: 0.002
+    weight_decay: 1e-6
+  scheduler: warmuplr     # pytorch v1.1.0+ required
+  scheduler_conf:
+    warmup_steps: 25000
+    lr_decay: 1.0
+  log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
+
+
+decoding:
+  batch_size: 128
+  error_rate_type: cer 
+  decoding_method: attention # 'attention', 'ctc_greedy_search', 'ctc_prefix_beam_search', 'attention_rescoring'
+  lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
+  alpha: 2.5
+  beta: 0.3
+  beam_size: 10
+  cutoff_prob: 1.0
+  cutoff_top_n: 0
+  num_proc_bsearch: 8
+  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/dataset/aishell/aishell.py

@@ -22,6 +22,7 @@ import argparse
 import codecs
 import json
 import os
+from pathlib import Path
 
 import soundfile
 
@@ -81,6 +82,8 @@ def create_manifest(data_dir, manifest_path_prefix):
                 # if no transcription for audio then skipped
                 if audio_id not in transcript_dict:
                     continue
+               
+                utt2spk = Path(audio_path).parent.name
                 audio_data, samplerate = soundfile.read(audio_path)
                 duration = float(len(audio_data) / samplerate)
                 text = transcript_dict[audio_id]
@@ -88,6 +91,7 @@ def create_manifest(data_dir, manifest_path_prefix):
                     json.dumps(
                         {
                             'utt': audio_id,
+                            'utt2spk': str(utt2spk),
                             'feat': audio_path,
                             'feat_shape': (duration, ),  # second
                             'text': text