metadata

language:
  - en
thumbnail: null
tags:
  - automatic-speech-recognition
  - Transducer
  - Conformer
  - pytorch
  - speechbrain
license: apache-2.0
datasets:
  - librispeech_asr
metrics:
  - wer
model-index:
  - name: Conformer-Transducer by SpeechBrain
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: LibriSpeech (clean)
          type: librispeech_asr
          config: clean
          split: test
          args:
            language: en
        metrics:
          - name: Test WER (non-streaming greedy)
            type: wer
            value: 2.72
          - name: Test WER (960ms chunk size, 4 left context chunks)
            type: wer
            value: 3.13
      - 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 (non-streaming greedy)
            type: wer
            value: 6.47

Conformer for LibriSpeech

This repository provides all the necessary tools to perform automatic speech recognition from an end-to-end system pretrained on LibriSpeech (EN) within SpeechBrain. For a better experience, we encourage you to learn more about SpeechBrain. The performance of the model in full context mode (no streaming) is the following:

Release	Test clean WER	Test other WER	GPUs
24-02-26	2.72	3.13	4xA100 40GB

With streaming, the results with different chunk sizes on test-clean are the following:

	full	cs=32 (1280ms)	24 (960ms)	16 (640ms)	12 (480ms)	8 (320ms)
full	2.72%	-	-	-	-	-
lc=32	-	3.09%	3.07%	3.26%	3.31%	3.44%
16	-	3.10%	3.07%	3.27%	3.32%	3.50%
8	-	3.10%	3.11%	3.31%	3.39%	3.62%
4	-	3.12%	3.13%	3.37%	3.51%	3.80%
2	-	3.19%	3.24%	3.50%	3.79%	4.38%

Pipeline description

This ASR system is a Conformer model trained with the RNN-T loss (with an auxiliary CTC loss to stabilize training). The model operates with a unigram tokenizer. Architecture details are described in the training hyperparameters file.

The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling transcribe_file if needed.

Install SpeechBrain

First of all, please install SpeechBrain with the following command:

pip install speechbrain

Please notice that we encourage you to read our tutorials and learn more about SpeechBrain.

Transcribing your own audio files (in English)

from speechbrain.inference.ASR import StreamingASR
from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
asr_model = StreamingASR.from_hparams(
    source="speechbrain/asr-streaming-conformer-librispeech",
    savedir="pretrained_models/asr-streaming-conformer-librispeech"
)
asr_model.transcribe_file(
    "speechbrain/asr-streaming-conformer-librispeech/test-en.wav",
    # select a chunk size of ~960ms with 4 chunks of left context
    DynChunkTrainConfig(24, 4),
    # disable torchaudio streaming to allow fetching from HuggingFace
    # set this to True for your own files or streams to allow for streaming file decoding
    use_torchaudio_streaming=False,
)

Commandline tool to transcribe a file or a live stream

Decoding from a live stream using ffmpeg (BBC Radio 4):

python3 asr.py http://as-hls-ww-live.akamaized.net/pool_904/live/ww/bbc_radio_fourfm/bbc_radio_fourfm.isml/bbc_radio_fourfm-audio%3d96000.norewind.m3u8 --model-source=sdelangen/speechbrain-asr-conformer-test --device=cpu -v

Decoding from a file:

python3 asr.py some-english-speech.wav --model-source=sdelangen/speechbrain-asr-conformer-test --device=cpu -v

from argparse import ArgumentParser
import logging

parser = ArgumentParser()
parser.add_argument("audio_path")
parser.add_argument("--model-source", required=True)
parser.add_argument("--device", default="cpu")
parser.add_argument("--ip", default="127.0.0.1")
parser.add_argument("--port", default=9431)
parser.add_argument("--chunk-size", default=24, type=int)
parser.add_argument("--left-context-chunks", default=4, type=int)
parser.add_argument("--num-threads", default=None, type=int)
parser.add_argument("--verbose", "-v", default=False, action="store_true")
args = parser.parse_args()

if args.verbose:
    logging.getLogger().setLevel(logging.INFO)

logging.info("Loading libraries")

from speechbrain.inference.ASR import StreamingASR
from speechbrain.utils.dynamic_chunk_training import DynChunkTrainConfig
import torch

device = args.device

if args.num_threads is not None:
    torch.set_num_threads(args.num_threads)

logging.info(f"Loading model from \"{args.model_source}\" onto device {device}")

asr = StreamingASR.from_hparams(args.model_source, run_opts={"device": device})
config = DynChunkTrainConfig(args.chunk_size, args.left_context_chunks)

logging.info(f"Starting stream from URI \"{args.audio_path}\"")

for text_chunk in asr.transcribe_file_streaming(args.audio_path, config):
    print(text_chunk, flush=True, end="")

Inference on GPU

To perform inference on the GPU, add run_opts={"device":"cuda"} when calling the from_hparams method.

Parallel Inference on a Batch

TODO

Please, see this Colab notebook to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.

Training

TODO

The model was trained with SpeechBrain (Commit hash: 'f73fcc35'). To train it from scratch follow these steps:

Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

Run Training:

cd recipes/LibriSpeech/ASR/transformer
python train.py hparams/conformer_large.yaml --data_folder=your_data_folder

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

About SpeechBrain

Website: https://speechbrain.github.io/
Code: https://github.com/speechbrain/speechbrain/
HuggingFace: https://huggingface.co/speechbrain/

Citing SpeechBrain

Please, cite SpeechBrain if you use it for your research or business.

@misc{speechbrain,
  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
  year={2021},
  eprint={2106.04624},
  archivePrefix={arXiv},
  primaryClass={eess.AS},
  note={arXiv:2106.04624}
}