metadata

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

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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 = 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": None,  # duration of the audio
    "taskname": "asr",  # use "ast" for speech-to-text translation
    "source_lang": "en",  # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
    "target_lang": "en",  # language of the text output, choices=['en','de','es','fr']
    "pnc": "yes",  # whether to have PnC output, choices=['yes', 'no'] 
}

and then use:

predicted_text = canary_model.transcribe(
    "<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": None,  # duration of the audio
    "taskname": "asr",  
    "source_lang": "en", # language of the audio input, set `source_lang`==`target_lang` for ASR, choices=['en','de','es','fr']
    "target_lang": "en", # language of the text output, choices=['en','de','es','fr']
    "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": None,  # duration of the audio
    "taskname": "ast",  
    "source_lang": "en", # language of the audio input, choices=['en','de','es','fr']
    "target_lang": "de", # language of the text output, choices=['en','de','es','fr']
    "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. 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.

The constituents of public data are as follows.

English (25.5k hours)

Librispeech 960 hours
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
Mozilla Common Voice (v11.0) - 1,474 hour subset

German (2.5k hours)

Mozilla Common Voice (v12.0) - 800 hour subset
Multilingual Librispeech (MLS DE) - 1,500 hour subset
VoxPopuli (DE) - 200 hr subset

Spanish (1.4k hours)

Mozilla Common Voice (v12.0) - 395 hour subset
Multilingual Librispeech (MLS ES) - 780 hour subset
VoxPopuli (ES) - 108 hour subset
Fisher - 141 hour subset

French (1.8k hours)

Mozilla Common Voice (v12.0) - 708 hour subset
Multilingual Librispeech (MLS FR) - 926 hour subset
VoxPopuli (FR) - 165 hour subset

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), and we process the groundtruth and predicted text with whisper-normalizer.

WER on MCV-16.1 test set:

Version	Model	En	De	Es	Fr
1.23.0	canary-1b	7.97	4.61	3.99	6.53

WER on MLS test set:

Version	Model	En	De	Es	Fr
1.23.0	canary-1b	3.06	4.19	3.15	4.12

More details on evaluation can be found at HuggingFace ASR Leaderboard

AST Performance

We evaluate AST performance with BLEU score, and use native annotations with punctuation and capitalization in the datasets.

BLEU score on FLEURS test set:

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

BLEU score on COVOST-v2 test set:

Version	Model	De->En	Es->En	Fr->En
1.23.0	canary-1b	37.67	40.7	40.42

BLEU score on mExpresso test set:

Version	Model	En->De	En->Es	En->Fr
1.23.0	canary-1b	23.84	35.74	28.29

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

[2] Attention is all you need

[3] Google Sentencepiece Tokenizer

[4] NVIDIA NeMo Toolkit

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.