返回模型
说明文档
注意：此配方使用此 PR https://github.com/k2-fsa/icefall/pull/349 中的代码进行训练
基于 icefall 的 WenetSpeech 数据集预训练 Transducer-Stateless2 模型
该模型使用 icefall 中的脚本，基于最新版本的 k2，在 WenetSpeech 的 L 子集上进行训练。
训练流程
主要代码库如下，我们将随着版本更新同步更新训练和解码脚本。
k2: https://github.com/k2-fsa/k2 icefall: https://github.com/k2-fsa/icefall lhotse: https://github.com/lhotse-speech/lhotse
- 安装 k2 和 lhotse，k2 安装指南请参考 https://k2.readthedocs.io/en/latest/installation/index.html，lhotse 请参考 https://lhotse.readthedocs.io/en/latest/getting-started.html#installation。我认为使用最新版本即可。同时请安装 icefall 中列出的依赖项。
- 克隆 icefall(https://github.com/k2-fsa/icefall) 并切换到上述显示的提交。
git clone https://github.com/k2-fsa/icefall
cd icefall
- 准备数据。
cd egs/wenetspeech/ASR
bash ./prepare.sh
- 训练
export CUDA_VISIBLE_DEVICES=\"0,1,2,3,4,5,6,7\"
./pruned_transducer_stateless2/train.py \
--world-size 8 \
--num-epochs 15 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--lang-dir data/lang_char \
--max-duration 180 \
--valid-interval 3000 \
--model-warm-step 3000 \
--save-every-n 8000 \
--training-subset L
评估结果
以下是 WenetSpeech（dev、test-net 和 test-meeting）上的解码结果（WER%），我们通过对第 9 到 10 轮的模型进行平均获得了此结果。
WER 如下：
| dev | test-net | test-meeting | 备注 | |
|---|---|---|---|---|
| 贪心搜索 | 7.80 | 8.75 | 13.49 | --epoch 10, --avg 2, --max-duration 100 |
| 改进的束搜索 (beam size 4) | 7.76 | 8.71 | 13.41 | --epoch 10, --avg 2, --max-duration 100 |
| 快速束搜索 (1best) | 7.94 | 8.74 | 13.80 | --epoch 10, --avg 2, --max-duration 1500 |
| 快速束搜索 | 9.82 | 10.98 | 16.37 | --epoch 10, --avg 2, --max-duration 600 |
| 快速束搜索 | 6.88 | 7.18 | 11.77 | --epoch 10, --avg 2, --max-duration 600 |
| 快速束搜索 | 14.94 | 16.14 | 22.93 | --epoch 10, --avg 2, --max-duration 600 |
luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless2
作者 luomingshuang
↓ 0
♥ 4
创建时间: 2022-05-19 14:32:27+00:00
更新时间: 2022-10-13 06:43:37+00:00
在 Hugging Face 上查看文件 (145)
.gitattributes
README.md
data/lang_char/LG.pt
data/lang_char/Linv.pt
data/lang_char/tokens.txt
data/lang_char/words.txt
exp/cpu_jit_epoch_10_avg_2_torch_1.11.0.pt
exp/cpu_jit_epoch_10_avg_2_torch_1.7.1.pt
exp/decoder-epoch-10-avg-2.onnx
ONNX
exp/encoder-epoch-10-avg-2.onnx
ONNX
exp/export.sh
exp/joiner-epoch-10-avg-2.onnx
ONNX
exp/joiner_decoder_proj-epoch-10-avg-2.onnx
ONNX
exp/joiner_encoder_proj-epoch-10-avg-2.onnx
ONNX
exp/pretrained_epoch_10_avg_2.pt
exp_trained_with_M/pretrained_epoch_29_avg_11.pt
exp_trained_with_S/pretrained_epoch_29_avg_24.pt
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