stt_zgh_fastconformer_ctc_small

Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>

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['asr']

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

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("ayymen/stt_zgh_fastconformer_ctc_small")

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

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="ayymen/stt_zgh_fastconformer_ctc_small"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Model Architecture

<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>

Training

The model was fine-tuned from an older checkpoint on a NVIDIA GeForce RTX 4050 Laptop GPU.

Datasets

Common Voice 18 kab and zgh splits, Tatoeba (kab, ber, shy), and bible readings in Tachelhit and Tarifit.

Performance

Metrics are computed on the cleaned, non-punctuated test sets of zgh and kab (converted to Tifinagh).

Limitations

<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>

Eg: 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.

References

<ADD ANY REFERENCES HERE AS NEEDED>

[1] NVIDIA NeMo Toolkit