stt_ml_fastconformer_ctc_med_punct

Stt Ml Fastconformer Ctc Med Punct

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Put a short model description here.

See the model architecture section and NeMo documentation for complete architecture details.

NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install NVIDIA NeMo. We recommend you install it after you've installed latest Pytorch version.

pip install nemo_toolkit['all']

How to Use this Model

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.

Automatically instantiate the model

NOTE: Please update the model class below to match the class of the model being uploaded.

import nemo.core import ModelPT
model = ModelPT.from_pretrained("kavyamanohar/stt_ml_fastconformer_ctc_med_punct")

NOTE

Add some information about how to use the model here. An example is provided for ASR inference below.

### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```

### Transcribing many audio files

```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py      pretrained_name="kavyamanohar/stt_ml_fastconformer_ctc_med_punct"      audio_dir=""
```

Input

Add some information about what are the inputs to this model

Output

Add some information about what are the outputs of this model

Model Architecture

Add information here discussing architectural details of the model or any comments to users about the model.

Training

Add information here about how the model was trained. It should be as detailed as possible, potentially including the the link to the script used to train as well as the base config used to train the model. If extraneous scripts are used to prepare the components of the model, please include them here.

NOTE

An example is provided below for ASR

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_transducer/speech_to_text_rnnt_bpe.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/fastconformer/fast-conformer_transducer_bpe.yaml).

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).

Datasets

Try to provide as detailed a list of datasets as possible. If possible, provide links to the datasets on HF by adding it to the manifest section at the top of the README (marked by ---).

NOTE

An example for the manifest section is provided below for ASR datasets

datasets:
- librispeech_asr
- fisher_corpus
- Switchboard-1
- WSJ-0
- WSJ-1
- National-Singapore-Corpus-Part-1
- National-Singapore-Corpus-Part-6
- vctk
- voxpopuli
- europarl
- multilingual_librispeech
- mozilla-foundation/common_voice_8_0
- MLCommons/peoples_speech

The corresponding text in this section for those datasets is stated below -

The model was trained on 64K hours of English speech collected and prepared by NVIDIA NeMo and Suno teams.

The training dataset consists of private subset with 40K hours of English speech plus 24K hours from the following public datasets:

- Librispeech 960 hours of English speech
- Fisher Corpus
- Switchboard-1 Dataset
- WSJ-0 and WSJ-1
- National Speech Corpus (Part 1, Part 6)
- VCTK
- VoxPopuli (EN)
- Europarl-ASR (EN)
- Multilingual Librispeech (MLS EN) - 2,000 hour subset
- Mozilla Common Voice (v7.0)
- People's Speech  - 12,000 hour subset

Performance

**Add information here about the performance of the model. Discuss what is the metric that is being used to evaluate the model and if there are external links explaning the custom metric, please link to it.

NOTE

An example is provided below for ASR metrics list that can be added to the top of the README

model-index:
- name: PUT_MODEL_NAME
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: AMI (Meetings test)
      type: edinburghcstr/ami
      config: ihm
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 17.10
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Earnings-22
      type: revdotcom/earnings22
      split: test
      args:
        language: en
    metrics:
    - name: Test WER
      type: wer
      value: 14.11

Provide any caveats about the results presented in the top of the discussion so that nuance is not lost.

It should ideally be in a tabular format (you can use the following website to make your tables in markdown format - https://www.tablesgenerator.com/markdown_tables)**

Limitations

Discuss any practical limitations to the model when being used in real world cases. They can also be legal disclaimers, or discussion regarding the safety of the model (particularly in the case of LLMs).

Note

An example is provided below 

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.

License

License to use this model is covered by the CC-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 license.

References

Provide appropriate references in the markdown link format below. Please order them numerically.

[1] NVIDIA NeMo Toolkit