README.md

metadata

language:
  - de
thumbnail: null
pipeline_tag: automatic-speech-recognition
tags:
  - whisper
  - pytorch
  - speechbrain
  - Transformer
license: apache-2.0
datasets:
  - RescueSpeech
metrics:
  - wer
  - sisnri
  - sdri
  - pesq
  - stoi
model-index:
  - name: noisy-whisper-resucespeech
    results:
      - task:
          name: Noise Robust Automatic Speech Recognition
          type: noise-robust-automatic-speech-recognition
        dataset:
          name: RescueSpeech
          type: zenodo.org/record/8077622
          config: de
          split: test
          args:
            language: de
        metrics:
          - name: Test WER
            type: wer
            value: '24.20'
          - name: Test PESQ
            type: pesq
            value: '2.085'
          - name: Test SI-SNRi
            type: si-snri
            value: '7.334'
          - name: Test SI-SDRi
            type: si-sdri
            value: '7.871'
inference: false

Noise robust speech recognition on jointly trained SepFormer speech enhancement and Whisper ASR using RescueSpeech data.

This repository provides all the necessary tools to perform noise robust automatic speech recognition on a simple combination of an enhancement model (SepFormer) and speech recognizer (Whisper). Initially, the models are fine-tuned individually on the RescueSpeech dataset, and then they are integrated to undergo joint training, enabling them to effectively handle noise interference. For a better experience, we encourage you to learn more about SpeechBrain.

The performance of the model is the following on test set:

Release	SISNRi	SDRi	PESQ	STOI	WER	GPUs
07-11-23	7.334	7.871	2.085	0.857	24.20	1xA100 80 GB

Pipeline description

• The enhancement system is composed of SepFormer model.
  • The model is first trained on Microsoft-DNS dataset and subsequently fine-tuned on RescueSpeech dataset.
  • The enhanced utterances are fed to the ASR model.
• And the ASR system is composed of whisper encoder-decoder blocks:
  • The pretrained whisper-large-v2 encoder is frozen.
  • The pretrained Whisper tokenizer is used.
  • A pretrained Whisper-large-v2 decoder (openai/whisper-large-v2) is finetuned on RescueSpeech dataset. The obtained final acoustic representation is given to the greedy decoder.

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 tranformers and 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 German)

from speechbrain.pretrained import SepformerSeparation as Separator
from speechbrain.pretrained import WhisperASR
import torch

enh_model = Separator.from_hparams(
                                source="speechbrain/noisy-whisper-resucespeech", 
                                savedir='pretrained_models/noisy-whisper-rescuespeech',
                                hparams_file="enhance.yaml"
                                )
asr_model = WhisperASR.from_hparams(
                                  source="speechbrain/noisy-whisper-resucespeech", 
                                  savedir="pretrained_models/noisy-whisper-rescuespeech",
                                  hparams_file="asr.yaml"
                                  )

# For custom file, change the path accordingly
est_sources = enh_model.separate_file(path='speechbrain/noisy-whisper-resucespeech/example_rescuespeech16k.wav')
pred_words, _ = asr_model(est_sources[:, :, 0], torch.tensor([1.0]))

Inference on GPU

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

You can find our training results (models, logs, etc) here.

Limitations

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

Referencing SpeechBrain

@misc{SB2021,
    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
    title = {SpeechBrain},
    year = {2021},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
  }

Referencing RescueSpeech

@misc{sagar2023rescuespeech,
    title={RescueSpeech: A German Corpus for Speech Recognition in Search and Rescue Domain},
    author={Sangeet Sagar and Mirco Ravanelli and Bernd Kiefer and Ivana Kruijff Korbayova and Josef van Genabith},
    year={2023},
    eprint={2306.04054},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}

About SpeechBrain

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.

Website: https://speechbrain.github.io/

GitHub: https://github.com/speechbrain/speechbrain