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--- |
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language: |
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- en |
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library_name: nemo |
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datasets: |
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- librispeech_asr |
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- fisher_corpus |
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- Switchboard-1 |
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- WSJ-0 |
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- WSJ-1 |
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- National Singapore Corpus Part 1 |
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- National Singapore Corpus Part 6 |
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- vctk |
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- VoxPopuli (EN) |
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- Europarl-ASR (EN) |
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- Multilingual LibriSpeech (2000 hours) |
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- mozilla-foundation/common_voice_8_0 |
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- MLCommons/peoples_speech |
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thumbnail: null |
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tags: |
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- automatic-speech-recognition |
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- speech |
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- audio |
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- CTC |
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- Conformer |
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- Transformer |
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- pytorch |
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- NeMo |
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- hf-asr-leaderboard |
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license: cc-by-4.0 |
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widget: |
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- example_title: Librispeech sample 1 |
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src: https://cdn-media.huggingface.co/speech_samples/sample1.flac |
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- example_title: Librispeech sample 2 |
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src: https://cdn-media.huggingface.co/speech_samples/sample2.flac |
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model-index: |
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- name: stt_en_conformer_transducer_xlarge |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: LibriSpeech (clean) |
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type: librispeech_asr |
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config: clean |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 1.62 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: LibriSpeech (other) |
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type: librispeech_asr |
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config: other |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 3.01 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: Multilingual LibriSpeech |
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type: facebook/multilingual_librispeech |
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config: english |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.32 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: Mozilla Common Voice 7.0 |
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type: mozilla-foundation/common_voice_7_0 |
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config: en |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.13 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: Mozilla Common Voice 8.0 |
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type: mozilla-foundation/common_voice_8_0 |
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config: en |
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split: test |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 6.46 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: Wall Street Journal 92 |
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type: wsj_0 |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 1.17 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: Wall Street Journal 93 |
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type: wsj_1 |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 2.05 |
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- task: |
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type: Automatic Speech Recognition |
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name: automatic-speech-recognition |
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dataset: |
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name: National Singapore Corpus |
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type: nsc_part_1 |
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args: |
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language: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 5.70 |
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--- |
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## Model Overview |
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This model transcribes speech in lower case English alphabet along with spaces and apostrophes. |
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It is a "extra-large" versions of Conformer-Transducer (around 600M parameters) model. |
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## NVIDIA NeMo: Training |
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To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version. |
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``` |
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pip install nemo_toolkit['all'] |
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``` |
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## NVIDIA Riva: Deployment |
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[CTC-based Conformers](https://huggingface.co/nvidia/stt_en_conformer_ctc_large) are supported by Riva today. This model, as well as other RNNT-based models, will be supported by future versions of [NVIDIA Riva](https://developer.nvidia.com/riva). |
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## How to Use this Model |
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The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. |
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### Automatically instantiate the model |
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```python |
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import nemo.collections.asr as nemo_asr |
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asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/stt_en_conformer_transducer_xlarge") |
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``` |
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### Transcribing using Python |
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First, let's get a sample |
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``` |
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wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav |
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``` |
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Then simply do: |
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``` |
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asr_model.transcribe(['2086-149220-0033.wav']) |
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``` |
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### Transcribing many audio files |
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```shell |
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python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py |
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pretrained_name="nvidia/stt_en_conformer_transducer_xlarge" |
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audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" |
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``` |
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### Input |
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This model accepts 16000 KHz Mono-channel Audio (wav files) as input. |
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### Output |
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This model provides transcribed speech as a string for a given audio sample. |
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## Model Architecture |
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Conformer-Transducer model is an autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses Transducer loss/decoding instead of CTC Loss. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html). |
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## Training |
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The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/asr_ctc/speech_to_text_ctc_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml). |
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The tokenizers for these models were built using the text transcripts of the train set with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py). |
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### Datasets |
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All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of several thousand hours of English speech: |
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- Librispeech 960 hours of English speech |
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- Fisher Corpus |
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- Switchboard-1 Dataset |
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- WSJ-0 and WSJ-1 |
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- National Speech Corpus (Part 1, Part 6) |
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- VCTK |
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- VoxPopuli (EN) |
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- Europarl-ASR (EN) |
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- Multilingual Librispeech (MLS EN) - 2,000 hrs subset |
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- Mozilla Common Voice (v8.0) |
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- People's Speech - 12,000 hrs subset |
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Note: older versions of the model may have trained on smaller set of datasets. |
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## Performance |
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The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. |
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| Version | Tokenizer | Vocabulary Size | LS test-other | LS test-clean | WSJ Eval92 | WSJ Dev93 | NSC Part 1 | MLS Test | MLS Dev | MCV Test 8.0 | Train Dataset | |
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|---------|-----------------------|-----------------|---------------|---------------|------------|-----------|-----|-------|------|----|------| |
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| 1.10.0 | SentencePiece Unigram | 1024 | 3.01 | 1.62 | 1.17 | 2.05 | 5.70 | 5.32 | 4.59 | 6.46 | NeMo ASRSET 3.0 | |
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## Limitations |
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Since this model was trained on publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech. |
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## References |
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[1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100) |
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[2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece) |
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[3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) |
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## Licence |
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License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license. |
