wav2vec2 large xlsr 53 polish

提供商jonatasgrosman
分类automatic-speech-recognition
许可证apache-2.0
下载量1.4K
星标0

简介

这是一个基于 Facebook wav2vec 2.0 架构并经过大规模多语言预训练（XLSR-53）的波兰语语音识别模型。它通过自监督学习在海量无标注音频上习得通用特征，再针对波兰语进行微调，能将语音高效转化为文本。对于需要处理波兰语语音转写、构建语音助手或进行音频分析的开发者来说，这是一个极具性价比的开源选择。它支持通过 Hugging Face 快速部署，上手难度低，且由于采用 Apache-2.0 协议，非常适合集成到商业项目中。

核心亮点

  • 专注于波兰语的高精度语音转文本
  • 基于 XLSR-53 强大的跨语言表征能力
  • 兼容 Hugging Face 生态，部署极其便捷
  • Apache-2.0 协议，支持商业化免费使用

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-polish")
tokenizer = AutoTokenizer.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-polish")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download jonatasgrosman/wav2vec2-large-xlsr-53-polish

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download jonatasgrosman/wav2vec2-large-xlsr-53-polish config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('jonatasgrosman/wav2vec2-large-xlsr-53-polish')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-polish

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-polish

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-polish')
tokenizer = AutoTokenizer.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-polish')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model jonatasgrosman/wav2vec2-large-xlsr-53-polish

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model jonatasgrosman/wav2vec2-large-xlsr-53-polish README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('jonatasgrosman/wav2vec2-large-xlsr-53-polish')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/jonatasgrosman/wav2vec2-large-xlsr-53-polish.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/jonatasgrosman/wav2vec2-large-xlsr-53-polish.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'jonatasgrosman/wav2vec2-large-xlsr-53-polish')

完整文档

来源: HuggingFace

---
language: pl
license: apache-2.0
datasets:

  • common_voice

  • mozilla-foundation/common_voice_6_0

metrics:
  • wer

  • cer

tags:
  • audio

  • automatic-speech-recognition

  • hf-asr-leaderboard

  • mozilla-foundation/common_voice_6_0

  • pl

  • robust-speech-event

  • speech

  • xlsr-fine-tuning-week

model-index:
  • name: XLSR Wav2Vec2 Polish by Jonatas Grosman

results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pl
type: common_voice
args: pl
metrics:
- name: Test WER
type: wer
value: 14.21
- name: Test CER
type: cer
value: 3.49
- name: Test WER (+LM)
type: wer
value: 10.98
- name: Test CER (+LM)
type: cer
value: 2.93
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pl
metrics:
- name: Dev WER
type: wer
value: 33.18
- name: Dev CER
type: cer
value: 15.92
- name: Dev WER (+LM)
type: wer
value: 29.31
- name: Dev CER (+LM)
type: cer
value: 15.17
---

Fine-tuned XLSR-53 large model for speech recognition in Polish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Polish using the train and validation splits of Common Voice 6.1.
When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

Usage

The model can be used directly (without a language model) as follows...

Using the HuggingSound library:

python
from huggingsound import SpeechRecognitionModel

model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-polish")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]


transcriptions = model.transcribe(audio_paths)

Writing your own inference script:

python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "pl"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-polish"
SAMPLES = 5


test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")


processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)


Preprocessing the datasets.


We need to read the audio files as arrays

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)


with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits


predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)


for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)

| Reference | Prediction |
| ------------- | ------------- |
| """CZY DRZWI BYŁY ZAMKNIĘTE?""" | PRZY DRZWI BYŁY ZAMKNIĘTE |
| GDZIEŻ TU POWÓD DO WYRZUTÓW? | WGDZIEŻ TO POM DO WYRYDÓ |
| """O TEM JEDNAK NIE BYŁO MOWY.""" | O TEM JEDNAK NIE BYŁO MOWY |
| LUBIĘ GO. | LUBIĄ GO |
| — TO MI NIE POMAGA. | TO MNIE NIE POMAGA |
| WCIĄŻ LUDZIE WYSIADAJĄ PRZED ZAMKIEM, Z MIASTA, Z PRAGI. | WCIĄŻ LUDZIE WYSIADAJĄ PRZED ZAMKIEM Z MIASTA Z PRAGI |
| ALE ON WCALE INACZEJ NIE MYŚLAŁ. | ONY MONITCENIE PONACZUŁA NA MASU |
| A WY, CO TAK STOICIE? | A WY CO TAK STOICIE |
| A TEN PRZYRZĄD DO CZEGO SŁUŻY? | A TEN PRZYRZĄD DO CZEGO SŁUŻY |
| NA JUTRZEJSZYM KOLOKWIUM BĘDZIE PIĘĆ PYTAŃ OTWARTYCH I TEST WIELOKROTNEGO WYBORU. | NAJUTRZEJSZYM KOLOKWIUM BĘDZIE PIĘĆ PYTAŃ OTWARTYCH I TEST WIELOKROTNEGO WYBORU |

Evaluation

1. To evaluate on mozilla-foundation/common_voice_6_0 with split test

bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-polish --dataset mozilla-foundation/common_voice_6_0 --config pl --split test

2. To evaluate on speech-recognition-community-v2/dev_data

bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-polish --dataset speech-recognition-community-v2/dev_data --config pl --split validation --chunk_length_s 5.0 --stride_length_s 1.0

Citation

If you want to cite this model you can use this:
bibtex
@misc{grosman2021xlsr53-large-polish,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}olish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-polish}},
  year={2021}
}
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