whisper small denoising

ProviderTaeyeun72
Categoryspeech-enhancement
LicenseApache-2.0
Downloads9
Stars0

Overview

The whisper-small-denoising model is a specialized speech enhancement tool designed to strip background noise from audio streams before they hit a transcription engine. Unlike general-purpose noise gates, this model is optimized to preserve the phonetic integrity of speech, making it an ideal pre-processing layer for ASR pipelines where audio quality is inconsistent. For developers, this means higher Word Error Rate (WER) stability in noisy environments without needing to manually tune audio filters. It integrates easily into Python-based audio workflows and operates under the permissive Apache-2.0 license, allowing for seamless commercial deployment.

Highlights

  • Reduces background noise to improve ASR accuracy
  • Optimized pre-processing for speech-to-text pipelines
  • Permissive Apache-2.0 license for commercial use
  • Maintains phonetic clarity during audio cleaning

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Taeyeun72/whisper-small-denoising")
tokenizer = AutoTokenizer.from_pretrained("Taeyeun72/whisper-small-denoising")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download Taeyeun72/whisper-small-denoising

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download Taeyeun72/whisper-small-denoising config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Taeyeun72/whisper-small-denoising')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/Taeyeun72/whisper-small-denoising

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Taeyeun72/whisper-small-denoising

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Taeyeun72/whisper-small-denoising')
tokenizer = AutoTokenizer.from_pretrained('Taeyeun72/whisper-small-denoising')

Full Documentation

来源: HuggingFace

---
language:

  • ko

base_model: openai/whisper-small-denoising
tags:
  • hf-asr-leaderboard

  • generated_from_trainer

datasets:
  • arrow

metrics:
  • wer

model-index:
  • name: whisper-kor3_de

results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: whisper-kor3_de
type: arrow
config: default
split: train
args: 'config: ko, split: valid'
metrics:
- name: Wer
type: wer
value: 31.549453628467354
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

whisper-kor3_de

This model is a fine-tuned version of openai/whisper-small-denoising on the whisper-kor3_de dataset.
It achieves the following results on the evaluation set:

  • Loss: 0.5477

  • Wer: 31.5495

  • Cer: 15.9235

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05

  • train_batch_size: 16

  • eval_batch_size: 16

  • seed: 42

  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

  • lr_scheduler_type: linear

  • lr_scheduler_warmup_steps: 100

  • training_steps: 1000

Training results

| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 1.366 | 0.21 | 50 | 1.0427 | 36.9851 | 17.8672 |
| 0.7035 | 0.42 | 100 | 0.7269 | 34.3514 | 17.0516 |
| 0.526 | 0.64 | 150 | 0.5843 | 35.7803 | 17.8672 |
| 0.5013 | 0.85 | 200 | 0.5556 | 33.1185 | 15.3365 |
| 0.403 | 1.06 | 250 | 0.5383 | 32.6142 | 15.2756 |
| 0.3058 | 1.27 | 300 | 0.5339 | 37.7136 | 18.9267 |
| 0.3081 | 1.48 | 350 | 0.5323 | 34.7716 | 17.3718 |
| 0.3131 | 1.69 | 400 | 0.5260 | 31.6615 | 15.8396 |
| 0.2857 | 1.91 | 450 | 0.5245 | 32.1098 | 14.8487 |
| 0.1625 | 2.12 | 500 | 0.5284 | 31.6895 | 14.8258 |
| 0.1899 | 2.33 | 550 | 0.5284 | 31.8577 | 15.8244 |
| 0.1646 | 2.54 | 600 | 0.5329 | 32.2499 | 16.4875 |
| 0.183 | 2.75 | 650 | 0.5315 | 31.5775 | 15.0393 |
| 0.179 | 2.97 | 700 | 0.5291 | 31.4374 | 14.7572 |
| 0.1048 | 3.18 | 750 | 0.5402 | 31.5775 | 14.8106 |
| 0.1057 | 3.39 | 800 | 0.5418 | 31.8296 | 15.7863 |
| 0.0965 | 3.6 | 850 | 0.5429 | 31.7456 | 15.9921 |
| 0.1098 | 3.81 | 900 | 0.5451 | 32.6422 | 16.8534 |
| 0.0902 | 4.03 | 950 | 0.5453 | 31.5495 | 15.9006 |
| 0.0795 | 4.24 | 1000 | 0.5477 | 31.5495 | 15.9235 |

Framework versions

  • Transformers 4.33.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.13.3
View official source
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