fromtransformersimportWhisperForConditionalGeneration model=WhisperForConditionalGeneration.from_pretrained(model_name_or_path,load_in_8bit=True,device_map="auto") We use the LoRA implementation from Hugging Face’speftpackage. There are four steps to fine-tune a model using LoRA: ...
必须是.wav格式)去噪python3scripts/denoise_audio.py# 分割并标注长音频python3scripts/long_audio_transcribe.py--languages"{PRETRAINED_MODEL}"--whisper_sizelarge# 标注短音频python3scripts/short_audio_transcribe.py--languages"{PRETRAINED_MODEL}"--whisper...
python evaluation.py --model_path=models/whisper-tiny-finetune --metric=cer 预测 执行以下程序进行语音识别,这个使用transformers直接调用微调后的模型或者Whisper原模型预测,支持Pytorch2.0的编译器加速、FlashAttention2加速、BetterTransformer加速。第一个--audio_path参数指定的是要预测的音频路径。第二个--model_...
至此环境准备完毕,可以按照项目内的数据格式和要求进行准备数据了。我这边finetune后的结果是正常的。如下:
Fine-tune on a custom datasetTo fine-tune a Whisper model on a custom dataset, the train/fine-tune_on_custom_dataset.py file can be used.Following is a sample command to perform the same:ngpu=4 # number of GPUs to perform distributed training on. torchrun --nproc_per_node=${ngpu}...
11 The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”. Fine-tuning Whisper in a Google Colab Prepare Environment We'll employ several popular Python packages to fine-tune the Whisper model. We'll use datasets[audio...
Whisper-Finetune 微调Whisper语音识别模型是一个强大的工具,用于加速语音识别模型的训练和推理。这个工具支持无时间戳数据训练,有时间戳数据训练、无语音数据训练。这意味着它能够适应不同的数据类型和环境,从而提供更好的性能和适应性。此外,Whisper-Finetune 还提供了加速推理的功能,使得模型在处理大量数据时能够更快...
生成的模型就存在 OUTPUT_MODEL 目录下面,同时在 config 生成了配置文件 modified_finetune_speaker.json 七 推理 训练完成或者中途要试听效果,可以暂停训练,然后用训练的模型进行推理。 执行后,如果没有报错,则会生成 gradio 的外网地址,浏览器打开这个地址 ...
Fine-tune in the Azure AI Foundry portal Create a custom speech model Custom speech datasets Custom speech model lifecycle How to use Pronunciation Assessment Improve recognition with phrase list Display text formatting Whisper model from OpenAI Speech to text FAQ Text to speech Speech translation I...
Run the training script to fine-tune the Whisper model: python main.py train --dataset_name google/fleurs --language fr_fr --num_train_epochs 10 --train_batch_size 4 --learning_rate 5e-5 --output_dir ./whisper-fr-LoRA --auth_token YOUR_HF_TOKEN Arguments: --dataset_name: Name of...