🚀 Whisper_Cleverlytics

Whisper_Cleverlytics是一款语音识别模型，基于openai/whisper-large-v3基础模型，使用Voice_Cleverlytics数据集进行训练，适用于阿拉伯语语音识别任务。

🚀 快速开始

若要运行该模型，首先需通过GitHub仓库安装Transformers库。

pip install --upgrade pip
pip install --upgrade git+https://github.com/huggingface/transformers.git accelerate datasets[audio]

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
#from datasets import load_dataset

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "smerchi/Arabic-Morocco-Speech_To_Text"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=False, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=16,
    return_timestamps=True,
    torch_dtype=torch_dtype,
    device=device,
)

audio="/content/audio.mp3"

%time result = pipe(audio)
print(result["text"],)

💻 使用示例

基础用法

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
#from datasets import load_dataset

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "smerchi/Arabic-Morocco-Speech_To_Text"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=False, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=16,
    return_timestamps=True,
    torch_dtype=torch_dtype,
    device=device,
)

audio="/content/audio.mp3"

%time result = pipe(audio)
print(result["text"],)

📚 详细文档

训练超参数

训练过程中使用了以下超参数：

• 学习率：1e-05
• 训练轮数：20

训练结果

文档未提供具体训练结果。

框架版本

• Transformers 4.35.2
• Pytorch 2.0.1+cu117
• Datasets 2.16.0
• Tokenizers 0.14.1

📄 许可证

本项目采用Apache-2.0许可证。