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https://github.com/k2-fsa/icefall.git
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change context size to 1
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Yuekai Zhang
parent
e7a2decfe4
commit
dc40220951
@ -74,7 +74,7 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare aishell2 manifest"
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# We assume that you have downloaded and unzip the aishell2 corpus
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# to $dl_dir/aishell2
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if [ ! -f data/manifests/.aishell_manifests.done ]; then
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if [ ! -f data/manifests/.aishell2_manifests.done ]; then
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mkdir -p data/manifests
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lhotse prepare aishell2 $dl_dir/aishell2 data/manifests -j $nj
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touch data/manifests/.aishell2_manifests.done
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@ -94,7 +94,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Compute fbank for aishell"
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log "Stage 3: Compute fbank for aishell2"
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if [ ! -f data/fbank/.aishell2.done ]; then
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mkdir -p data/fbank
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./local/compute_fbank_aishell2.py
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@ -129,7 +129,7 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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# The implementation of chinese word segmentation for text,
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# and it will take about 15 minutes.
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# If can't install paddle-tiny with python 3.8, please refer
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# If you can't install paddle-tiny with python 3.8, please refer to
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# https://github.com/fxsjy/jieba/issues/920
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if [ ! -f $lang_char_dir/text_words_segmentation ]; then
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python3 ./local/text2segments.py \
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@ -149,4 +149,4 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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if [ ! -f $lang_char_dir/L_disambig.pt ]; then
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python3 ./local/prepare_char.py
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fi
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fi
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fi
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File diff suppressed because it is too large
Load Diff
1
egs/aishell2/ASR/pruned_transducer_stateless5/conformer.py
Symbolic link
1
egs/aishell2/ASR/pruned_transducer_stateless5/conformer.py
Symbolic link
@ -0,0 +1 @@
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../../../librispeech/ASR/pruned_transducer_stateless5/conformer.py
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@ -1,65 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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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"""
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To run this file, do:
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cd icefall/egs/librispeech/ASR
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python ./pruned_transducer_stateless4/test_model.py
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"""
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from train import get_params, get_transducer_model
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def test_model_1():
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params = get_params()
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params.vocab_size = 500
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params.blank_id = 0
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params.context_size = 2
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params.num_encoder_layers = 24
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params.dim_feedforward = 1536 # 384 * 4
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params.encoder_dim = 384
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model = get_transducer_model(params)
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num_param = sum([p.numel() for p in model.parameters()])
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print(f"Number of model parameters: {num_param}")
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# See Table 1 from https://arxiv.org/pdf/2005.08100.pdf
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def test_model_M():
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params = get_params()
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params.vocab_size = 500
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params.blank_id = 0
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params.context_size = 2
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params.num_encoder_layers = 18
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params.dim_feedforward = 1024
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params.encoder_dim = 256
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params.nhead = 4
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params.decoder_dim = 512
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params.joiner_dim = 512
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model = get_transducer_model(params)
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num_param = sum([p.numel() for p in model.parameters()])
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print(f"Number of model parameters: {num_param}")
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def main():
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# test_model_1()
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test_model_M()
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if __name__ == "__main__":
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main()
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@ -243,7 +243,7 @@ def get_parser():
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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default=1,
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help="The context size in the decoder. 1 means bigram; "
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"2 means tri-gram",
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)
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@ -398,7 +398,7 @@ def get_params() -> AttributeDict:
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"batch_idx_train": 0,
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"log_interval": 50,
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"reset_interval": 200,
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"valid_interval": 3000, # For the 100h subset, use 800
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"valid_interval": 3000,
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# parameters for conformer
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"feature_dim": 80,
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"subsampling_factor": 4,
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@ -524,9 +524,6 @@ def load_checkpoint_if_available(
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if "cur_epoch" in saved_params:
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params["start_epoch"] = saved_params["cur_epoch"]
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if "cur_batch_idx" in saved_params:
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params["cur_batch_idx"] = saved_params["cur_batch_idx"]
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return saved_params
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@ -748,12 +745,7 @@ def train_one_epoch(
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tot_loss = MetricsTracker()
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cur_batch_idx = params.get("cur_batch_idx", 0)
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for batch_idx, batch in enumerate(train_dl):
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if batch_idx < cur_batch_idx:
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continue
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cur_batch_idx = batch_idx
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params.batch_idx_train += 1
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batch_size = len(batch["supervisions"]["text"])
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@ -779,8 +771,9 @@ def train_one_epoch(
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scaler.update()
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optimizer.zero_grad()
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except: # noqa
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display_and_save_batch(batch, params=params,
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graph_compiler=graph_compiler)
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display_and_save_batch(
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batch, params=params, graph_compiler=graph_compiler
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)
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raise
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if params.print_diagnostics and batch_idx == 5:
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@ -801,7 +794,6 @@ def train_one_epoch(
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params.batch_idx_train > 0
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and params.batch_idx_train % params.save_every_n == 0
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):
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params.cur_batch_idx = batch_idx
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save_checkpoint_with_global_batch_idx(
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out_dir=params.exp_dir,
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global_batch_idx=params.batch_idx_train,
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@ -814,7 +806,6 @@ def train_one_epoch(
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scaler=scaler,
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rank=rank,
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)
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del params.cur_batch_idx
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remove_checkpoints(
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out_dir=params.exp_dir,
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topk=params.keep_last_k,
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@ -1113,8 +1104,9 @@ def scan_pessimistic_batches_for_oom(
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f"Failing criterion: {criterion} "
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f"(={crit_values[criterion]}) ..."
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)
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display_and_save_batch(batch, params=params,
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graph_compiler=graph_compiler)
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display_and_save_batch(
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batch, params=params, graph_compiler=graph_compiler
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)
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raise
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