1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
|
# Speech Recognition Inference with CTC Beam Search Decoder
This is an example inference script for running decoding on the LibriSpeech dataset and wav2vec 2.0 models, using a CTC beam search decoder that supports lexicon constraint and language model integration. The language model used is a 4-gram KenLM trained on the LibriSpeech dataset.
## Usage
Additional command line parameters and information can is available with the `--help` option.
Sample command
```
python inference.py \
--librispeech_path ./librispeech/ \
--split test-other \
--model WAV2VEC2_ASR_BASE_960H \
--beam-size 1500 \
--lm-weight 1.74 \
--word-score 0.52
```
## Results
The table below contains WER results for various pretrained models on LibriSpeech, using a beam size of 1500, and language model weight and word insertion scores taken from Table 7 of [wav2vec 2.0](https://arxiv.org/pdf/2006.11477.pdf).
| Model | test-clean | test-other |
|:------------------------------------------------------------------------------------------------:|-----------:|-----------:|
| [WAV2VEC2_ASR_BASE_10M](https://pytorch.org/audio/main/pipelines.html#wav2vec2-asr-base-10m) | 9.35| 15.91|
| [WAV2VEC2_ASR_BASE_100H](https://pytorch.org/audio/main/pipelines.html#wav2vec2-asr-base-100h) | 3.42| 8.07|
| [WAV2VEC2_ASR_BASE_960H](https://pytorch.org/audio/main/pipelines.html#wav2vec2-asr-base-960h) | 2.61| 6.15|
| [WAV2VEC2_ASR_LARGE_960H](https://pytorch.org/audio/main/pipelines.html#wav2vec2-asr-large-960h) | 2.34| 4.98|
|
