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.
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="ai-nightcoder/uzbek-speaker-verification-v3" 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.
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.
