license: cc-by-nc-4.0
language:
- en
- de
- es
- fr
library_name: nemo
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
thumbnail: null
tags:
- automatic-speech-recognition
- automatic-speech-translation
- speech
- audio
- Transformer
- FastConformer
- Conformer
- pytorch
- NeMo
- hf-asr-leaderboard
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: canary-1b
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 2.89
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: SPGI Speech
type: kensho/spgispeech
config: test
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 4.79
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: en
split: test
args:
language: en
metrics:
- name: Test WER (En)
type: wer
value: 7.97
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: de
split: test
args:
language: de
metrics:
- name: Test WER (De)
type: wer
value: 4.61
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: es
split: test
args:
language: es
metrics:
- name: Test WER (ES)
type: wer
value: 3.99
- task:
type: Automatic Speech Recognition
name: automatic-speech-recognition
dataset:
name: Mozilla Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: fr
split: test
args:
language: fr
metrics:
- name: Test WER (Fr)
type: wer
value: 6.53
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->De)
type: bleu
value: 22.66
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->Es)
type: bleu
value: 41.11
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: en_us
split: test
args:
language: en-de
metrics:
- name: Test BLEU (En->Fr)
type: bleu
value: 40.76
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: de_de
split: test
args:
language: de-en
metrics:
- name: Test BLEU (De->En)
type: bleu
value: 32.64
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: es_419
split: test
args:
language: es-en
metrics:
- name: Test BLEU (Es->En)
type: bleu
value: 32.15
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: FLEURS
type: google/fleurs
config: fr_fr
split: test
args:
language: fr-en
metrics:
- name: Test BLEU (Fr->En)
type: bleu
value: 23.57
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: de_de
split: test
args:
language: de-en
metrics:
- name: Test BLEU (De->En)
type: bleu
value: 37.67
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: es_419
split: test
args:
language: es-en
metrics:
- name: Test BLEU (Es->En)
type: bleu
value: 40.7
- task:
type: Automatic Speech Translation
name: automatic-speech-translation
dataset:
name: COVOST
type: covost2
config: fr_fr
split: test
args:
language: fr-en
metrics:
- name: Test BLEU (Fr->En)
type: bleu
value: 40.42
metrics:
- wer
- bleu
pipeline_tag: automatic-speech-recognition
Canary 1B
NVIDIA NeMo Canary is a family of multi-lingual multi-tasking models that achieves state-of-the art performance on multiple benchmarks. With 1 billion parameters, Canary-1B supports automatic speech-to-text recognition (ASR) in 4 languages (English, German, French, Spanish) and translation from English to German/French/Spanish and from German/French/Spanish to English with or without punctuation and capitalization (PnC).
Model Architecture
Canary is an encoder-decoder model with FastConformer [1] encoder and Transformer Decoder [2].
With audio features extracted from the encoder, task tokens such as <source language>, <target language>, <task> and <toggle PnC>
are fed into the Transformer Decoder to trigger the text generation process. Canary uses a concatenated tokenizer from individual
SentencePiece [3] tokenizers of each language, which makes it easy to scale up to more languages.
The Canay-1B model has 24 encoder layers and 24 layers of decoder layers in total.
NVIDIA NeMo
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 Cython and latest PyTorch version.
pip install git+https://github.com/NVIDIA/[email protected]#egg=nemo_toolkit[all]
How to Use this Model
The model is available for use in the NeMo toolkit [4], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
Loading the Model
from nemo.collections.asr.models import EncDecMultiTaskModel
# load model
canary_model = EncDecMultiTaskModel.from_pretrained('nvidia/canary-1b')
# update dcode params
decode_cfg = canary_model.cfg.decoding
decode_cfg.beam.beam_size = 5 # default is greedy with beam_size=1
canary_model.change_decoding_strategy(decode_cfg)
Input Format
The input to the model can be a directory containing audio files, in which case the model will perform ASR on English and produces text with punctuation and capitalization:
predicted_text = canary_model.trancribe(
audio_dir="<path to directory containing audios>",
batch_size=16, # batch size to run the inference with
)
or use:
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/canary-1b"
audio_dir="<path to audio directory>"
Another recommended option is to use a json manifest as input, where each line in the file is a dictionary containing the following fields:
# Example of a line in input_manifest.json
{
"audio_filepath": "/path/to/audio.wav", # path to the audio file
"duration": 10000.0, # duration of the audio
"taskname": "asr", # use "s2t_translation" for AST
"source_lang": "en", # Set `source_lang`=`target_lang` for ASR, choices=['en','de','es','fr']
"target_lang": "de", # choices=['en','de','es','fr']
"pnc": yes, # whether to have PnC output, choices=['yes', 'no']
}
and then use:
predicted_text = canary_model.trancribe(
paths2audio_files="<path to input manifest file>",
batch_size=16, # batch size to run the inference with
)
or use:
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py
pretrained_name="nvidia/canary-1b"
dataset_manifest="<path to manifest file>"
Automatic Speech-to-text Recognition (ASR)
An example manifest for transcribing English audios can be:
# Example of a line in input_manifest.json
{
"audio_filepath": "/path/to/audio.wav", # path to the audio file
"duration": 10000.0, # duration of the audio
"taskname": "asr",
"source_lang": "en",
"target_lang": "en",
"pnc": yes, # whether to have PnC output, choices=['yes', 'no']
}
Automatic Speech-to-text Translation (AST)
An example manifest for transcribing English audios into German text can be:
# Example of a line in input_manifest.json
{
"audio_filepath": "/path/to/audio.wav", # path to the audio file
"duration": 10000.0, # duration of the audio
"taskname": "s2t_translation",
"source_lang": "en",
"target_lang": "de",
"pnc": yes, # whether to have PnC output, choices=['yes', 'no']
}
Input
This model accepts single channel (mono) audio sampled at 16000 Hz, along with the task/languages/PnC tags as input.
Output
The model outputs the transcribed/translated text corresponding to the input audio, in the specified target language and with or without punctuation and capitalization.
Training
Canary-1B is trained using the NVIDIA NeMo toolkit [4] for 150k steps with dynamic bucketing and a batch duration of 360s per GPU on 128 NVIDIA A100 80GB GPUs in 24 hrs. The model can be trained using this example script and base config.
The tokenizers for these models were built using the text transcripts of the train set with this script.
Datasets
The Canary-1B model is trained on a total of 85k hrs of speech data. It consists of 31k hrs of public data, 20k hrs collected by Suno, and 34k hrs of in-house data.
Performance
In both ASR and AST experiments, predictions were generated using beam search with width 5 and length penalty 1.0.
ASR Performance (w/o PnC)
The ASR performance is measured with word error rate (WER) on MCV-16.1 test sets on four languages, and we process the groundtruth and predicted text with whisper-normalizer.
| Version | Model | En | De | Es | Fr |
|---|---|---|---|---|---|
| 1.23.0 | canary-1b | 7.97 | 4.61 | 3.99 | 6.53 |
More details on evaluation can be found at HuggingFace ASR Leaderboard
AST Performance
We evaluate AST performance with BLEU score on the FLEURS test sets on four languages and use their native annotations with punctuation and capitalization.
| Version | Model | En->De | En->Es | En->Fr | De->En | Es->En | Fr->En |
|---|---|---|---|---|---|---|---|
| 1.23.0 | canary-1b | 22.66 | 41.11 | 40.76 | 32.64 | 32.15 | 23.57 |
NVIDIA Riva: Deployment
NVIDIA Riva, 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 Riva live demo.
References
[1] Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
[3] Google Sentencepiece Tokenizer
Licence
License to use this model is covered by the CC-BY-NC-4.0. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-NC-4.0 license.