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parakeet-ctc-1.1b is an ASR model that transcribes speech in lower case English alphabet. This model is jointly developed by
and
teams. It is an XXL version of FastConformer CTC [1] (around 1.1B parameters) model. See the
section and
for complete architecture details.
NVIDIA NeMo: Training
To train, fine-tune or play with the model you will need to install
. We recommend you install it after you've installed latest PyTorch version.
pip install nemo_toolkit['all'] How to Use this Model
There are several ways to use this model. Choose the one that fits your needs.
Run locally with NeMo-Speech.cpp
provides a lightweight native C++ runtime for local inference with this model. After
:
hf download nvidia/parakeet-ctc-1.1b \ parakeet-ctc-1.1b.q8_0.gguf \ --local-dir models nemo-speech transcribe audio.wav \ --model models/parakeet-ctc-1.1b.q8_0.gguf See the
for more details.
NVIDIA NeMo
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. Moreover, you can now run Parakeet CTC natively with
🤗.
Automatically instantiate the model
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="nvidia/parakeet-ctc-1.1b") Transcribing using NeMo
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 using
🤗
Make sure to install transformers from source.
pip install git+https://github.com/huggingface/transformers ➡️ Pipeline usagefrom transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-ctc-1.1b") out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3") print(out) ➡️ AutoModelfrom transformers import AutoModelForCTC, AutoProcessor from datasets import load_dataset, Audio import torch device = "cuda"if torch.cuda.is_available() else"cpu" processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b") model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) speech_samples = [el['array'] for el in ds["audio"][:5]] inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate) inputs.to(model.device, dtype=model.dtype) outputs = model.generate(**inputs) print(processor.batch_decode(outputs)) ➡️ Trainingfrom transformers import AutoModelForCTC, AutoProcessor from datasets import load_dataset, Audio import torch device = "cuda"if torch.cuda.is_available() else"cpu" processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b") model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device) ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) speech_samples = [el['array'] for el in ds["audio"][:5]] text_samples = [el for el in ds["text"][:5]] # passing `text` to the processor will prepare inputs' `labels` key inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_extractor.sampling_rate) inputs.to(device, dtype=model.dtype) outputs = model(**inputs) outputs.loss.backward() For more details about usage, the refer to
.
Transcribing many audio files
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py pretrained_name="nvidia/parakeet-ctc-1.1b" audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" Input
This model accepts 16000 Hz mono-channel audio (wav files) as input.
Output
This model provides transcribed speech as a string for a given audio sample.
Model Architecture
FastConformer [1] is an optimized version of the Conformer model with 8x depthwise-separable convolutional downsampling. The model is trained using CTC loss. You may find more information on the details of FastConformer here:
.
Training
The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this
and this
.
The tokenizers for these models were built using the text transcripts of the train set with this
.
Datasets
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
The performance of Automatic Speech Recognition models is measuring using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.
The following tables summarizes the performance of the available models in this collection with the CTC decoder. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding.
VersionTokenizerVocabulary SizeAMIEarnings-22Giga SpeechLS test-cleanSPGI SpeechTEDLIUM-v3Vox PopuliCommon Voice1.22.0SentencePiece Unigram102415.6213.6910.271.833.544.203.546.53These are greedy WER numbers without external LM. More details on evaluation can be found at
NVIDIA Riva: Deployment
, is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides:
World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours
Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support
Although this model isn’t supported yet by Riva, the
list of supported models is here
.
Check out
.
References
[1]
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
[2]
Google Sentencepiece Tokenizer
[3]
[4]
[5]
Licence
License to use this model is covered by the
. By downloading the public and release version of the model, you accept the terms and conditions of the
license.