No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.
Cataloged from nvidia/stt_en_citrinet_256_ls
All models by nvidia →|
|
|
| |
This model transcribes speech in lower case English alphabet along with spaces and apostrophes.
It is an "extra-small" versions of Citrinet-CTC (around 10M parameters) model.
See the model architecture section and NeMo documentation for complete architecture details.
It is also compatible with NVIDIA Riva for production-grade server deployments.
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.
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.
First, let's get a sample
Then simply do:
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
This model provides transcribed speech as a string for a given audio sample.
Citrinet-CTC model is an autoregressive variant of Citrinet model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer Loss. You may find more info on the detail of this model here: Citrinet Model.
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config (Note: Change the model.model_defaults.filters to match the model size).
The tokenizers for these models were built using the text transcripts of the train set with this script.
All the models in this collection are trained on a just the Librispeech Dataset:
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.
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.
For the best real-time accuracy, latency, and throughput, deploy the model with NVIDIA Riva, an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded. Additionally, Riva provides:
Check out Riva live demo.
[1] Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition [2] Google Sentencepiece Tokenizer [3] NVIDIA NeMo Toolkit
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.
pip install nemo_toolkit['all']import nemo.collections.asr as nemo_asrasr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_en_citrinet_256_ls")wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wavoutput = asr_model.transcribe(['2086-149220-0033.wav'])print(output[0].text)python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/stt_en_citrinet_256_ls" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"