另外项目最后还支持CTranslate2加速推理和GGML加速推理,提示一下,加速推理支持直接使用Whisper原模型转换,并不一定需要微调。支持Windows桌面应用,Android应用和服务器部署。 请先点star 支持模型 openai/whisper-tiny openai/whisper-base openai/whisper-small openai/whisper-me
另外项目最后还支持CTranslate2加速推理和GGML加速推理,提示一下,加速推理支持直接使用Whisper原模型转换,并不一定需要微调。支持Windows桌面应用,Android应用和服务器部署。 在线试用 使用whisper-small微调后的模型:在线演示 支持模型 openai/whisper-tiny openai/whisper-base openai/whisper-small openai/whisper-medium ...
Our fine-tuned model significantly improves upon the zero-shot performance of the Whisper small checkpoint, highlighting the strong transfer learning capabilities of Whisper.We can automatically submit our checkpoint to the leaderboard when we push the training results to the Hub - we simply have t...
Bert-vits2转写和标注独立整合包,整合阿里FunAsr,必剪Asr以及Whisper大模型 04:43 GPT-SoVits,效果炸裂复刻HeyGen核心功能,几乎零训练成本的跨语言音色克隆模型 10:06 GPT-SoVits,HeyGen平替,老外讲中文,英语素材模型测试(Emma Watson) 07:21 转写标注整合包,集成阿里Asr三语支持,支持GPT-SoVits和Bert-vits...
whisper_size: 可选择medium和large 2.5 划分训练/测试集并标注 #@markdown 运行该单元格会生成划分好训练/测试集的最终标注,以及配置文件#@markdown Running this block will generate final annotations for training & validation, as well as config file.#@markdown 选择是否加入辅助训练数据:/ Choose whether ...
fromtransformersimportWhisperTokenizer tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Hindi", task="transcribe") Tip:the blog post can be adapted forspeech translationby setting the task to"translate"and the language to the target text language in the above line. This...
: 15737-15749.[R2] Xu, Tian, et al. "Understanding Adversarial Imitation Learning in Small ...
We'll start our fine-tuning run from the pre-trained Whisper small checkpoint. To do this, we'll load the pre-trained weights from the Hugging Face Hub. Again, this is trivial through use of 🤗 Transformers! from transformers import WhisperForConditionalGeneration model = Whispe...
We'll start our fine-tuning run from the pre-trained Whisper small checkpoint. To do this, we'll load the pre-trained weights from the Hugging Face Hub. Again, this is trivial through use of 🤗 Transformers! from transformers import WhisperForConditionalGeneration model = WhisperForC...
We'll start our fine-tuning run from the pre-trained Whisper small checkpoint. To do this, we'll load the pre-trained weights from the Hugging Face Hub. Again, this is trivial through use of 🤗 Transformers!from transformers import WhisperForConditionalGeneration model = WhisperForConditional...