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: ...
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-segformer.md fine-tune-vit.md fine-tune-w2v2-bert.md fine-tune-wav2vec2-english.md fine-tune-whisper.md fine-tune-xlsr-wav2vec2.md fl-with-flower.md game-jam-first-edition-results.md game-jam.md gaussian-splatting.md gcp-partnership.md gemma-peft.md gemma.md ge...
[WFTE] Fine-tune Whisper with Jupyter - Part 1, 视频播放量 24、弹幕量 0、点赞数 2、投硬币枚数 0、收藏人数 0、转发人数 0, 视频作者 HuggingFace, 作者简介 The AI community building the future.,相关视频:[WFTE] Fine-tune Whisper with Jupyter - Part 2,[WFTE
inputs will be cast from torch.float32 to float16 during quantization warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization") Traceback (most recent call last): File "finetune.py", line 176, in <module> whisper_finetune(traindir,devdir,outdi...
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对预训练数据处理,通过 Whisper 生成标注数据。 五 数据质量 对于训练数据少于 100 条的或质量不高的,可以勾选那个 「ADD_AUXILIARY 」选项。 六 训练 准备工作都好了后,就可以进行训练了,可以指定训练的轮数,一般至少要 200 轮,这个根据素材和实际情况调整就行了,如果训练完后,推理的试听效果不佳,也可以继续...
主要内容是如何使用本地服务器基于VITS微调(finetune)出想要的语音生成模型,并通过网页UI进行体验(也支持本地部署); 1 准备 1.1 硬件 带GPU的机器(服务器、PC、或google-colab) 内存:16GB+ 显存:16GB+ 建议使用N卡(我的方案是带一张T4的服务器) 1.2 系统要求 操作系统:linux python:3.9 1.3 环境依赖 1.3.1...
One such model is the Whisper ASR model developed by OpenAI, which is based on a Transformer encoder-decoder architecture and can handle multiple tasks such as language identification, transcription, and translation. However, there are still limitations to the Whisper ASR model, such...
Output Data whisper-small-np chevron_right folder checkpoint-17910empty folder checkpoint-7164empty folder runsempty Outputmore_vert arrow_drop_down folder whisper-small-np arrow_right folder checkpoint-17910 arrow_right folder checkpoint-7164 arrow_right folder runs Download notebook output navigate_...