Whisper huggingface. The original code repository can be found here.
Whisper huggingface If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx). 2319; eval_runtime: 629. In this notebook, we will utilize the Whisper model provided by Hugging Face to transcribe both a sample audio from a dataset and optionally from a microphone recording. Results Aishell training results (Fine-tuning Pretrained Models) Whisper fine-tuning results on Aishell test set on whisper medium, large-v2, large-v3 Nov 28, 2024 · A short note about converting Whisper ASR model from HuggingFace transformers for direct usage in PyTorch. Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Example Whisper's performance varies widely depending on the language. 8448; eval_wer: 42. mp3") print (result["text"]) Internally, the transcribe() method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window. Usage 💬 (command line) English Run whisper on example segment (using default params, whisper small) add --highlight_words True to visualise word timings in the . Dysarthric speaker embeddings with Pyannote. whisperx examples Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library). Distil-Whisper is a distilled version of Whisper for English speech recognition that is 6 times faster, 49% smaller, and performs within 1% word error rate (WER) on out-of-distribution evaluation sets: Whisper is a state-of-the-art model for automatic speech recognition and speech translation, trained on >5M hours of weakly labeled audio. This is the third and final installment of the Distil-Whisper English series. import whisper model = whisper. . Contribute to ggerganov/whisper. 22, eval Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library). Whisper-Large-V3-French Whisper-Large-V3-French is fine-tuned on openai/whisper-large-v3 to further enhance its performance on the French language. load_model("turbo") result = model. Grab you huggingface access token and login so you are certainly able to download the This repository provides an optimized JAX model for the Indic Whisper Model, built upon the foundation of the 🤗 Indic Whisper implementation by AI4 Bharat. from_pretrained(“openai/whisper-base”) and I have a question that may not I have extracted the hidden layer outputs via Kotoba-Whisper-Bilingual (v1. WhisperProcessor offers all the functionalities of WhisperFeatureExtractor and WhisperTokenizer . Whisper is a pre-trained model for automatic speech recognition and speech translation, trained on 680k hours of labelled data. 5956; eval_wer_ortho: 42. The model first converts speech to spectrograms, then uses an auto-regressive transformer to decode the speech to text. This type can be changed when the model is loaded using the compute_type option in CTranslate2. Usage Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. co/openai/whisper-small with ONNX weights to be compatible with Transformers. - huggingface/peft https://huggingface. Feb 27, 2024 · Hey @sanchit-gandhi-I’ve been following your Fine Tuning Whisper blog and forums posts all over hugging face and i’ve been trying to fine tune Whisper’s medium-en model on some of my own datasets with some success but mostly failures. Japanese ASR; English ASR; Speech-to-text translation (Japanese -> English) Speech-to-text translation (English -> Japanese) developed through the collaboration bewteen Asahi Ushio and However, due to the different implementation of the timestamp calculation in faster whisper or more precisely CTranslate2 the timestamp accuracy can not be guaranteed. co/openai/whisper-large with ONNX weights to be compatible with Transformers. More information Nov 3, 2022 · In this blog, we present a step-by-step guide on fine-tuning Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers. 7256; eval_samples_per_second: 1. In this blog, we present a step-by-step guide on fine-tuning Whisper for any multilingual ASR dataset using Hugging Face 🤗 Transformers. The model was trained and evaluated only on English data. Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library). This blog provides in-depth explanations of the Whisper model, the Common Voice dataset and the theory behind fine-tuning, with accompanying code cells to execute the data preparation and fine-tuning steps. WebNN changes This original model is Whisper-base. A pretrained Whisper-large-v2 decoder (openai/whisper-large-v2) is finetuned on RescueSpeech dataset. Nov 14, 2024 · Distil-Whisper [4] was developed by HuggingFace in 2023. It leverages common knowledge distillation techniques to train the smaller model, such as pseudo-labeling from the Whisper Large model and Kullback-Leibler Divergence loss. REST API If you're interested in deploying this app as a REST API, please check out /backend . It is called automatically for Mobius Labs fork of faster-whisper. Sep 3, 2024 · The fine-tuned model can be loaded just like the original Whisper model via the HuggingFace from_pretrained() function. We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language. Usage Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Running App Files Files Community 6 Refreshing. ai to improve Hebrew ASR using crowd-sourced labeling. load_model() function, but it only accepts strings like "small", "base", e Jul 26, 2023 · Hi @sanchit-gandhi @MariaK, I have been following the guide to fine-tune whisper in the course and also looking at this blog. It is trained on a large dataset of diverse audio and uses a Transformer sequence-to-sequence model with special tokens for different tasks. json --quantization float16 Note that the model weights are saved in FP16. 此处可能存在不合适展示的内容，页面不予展示。您可通过相关编辑功能自查并修改。 如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容，可点击提交进行申诉，我们将尽快为您处理。 NB-Whisper Base Introducing the Norwegian NB-Whisper Base model, proudly developed by the National Library of Norway. from OpenAI. However, if we need to make it support new language (which is not supported by the tokenizer), how could I do that? Could you please point me to the document or example which I could follow? OpenAI's Whisper model is a cutting-edge automatic speech recognition (ASR) system designed to convert spoken language into text. I’m not well learned on ML/AI but I am able to get around. 5x more epochs with added regularization for improved performance. Whisper Small PS - Hanif Rahman This model is a fine-tuned version of openai/whisper-large-v3-turbo on the Common Voice 17. Whisper 模型要求输入为对数梅尔声谱图。 梅尔频段是语音处理的标准方法，研究人员用它来近似表示人类的听觉范围。对于 Whisper 微调这个任务而言，我们只需要知道声谱图是语音信号中频率的直观表示。更多有关梅尔频段的详细信息，请参阅 梅尔倒谱 一文。 Whisper Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. Note: Having a separate repo for ONNX weights is intended to be Apr 20, 2023 · Free MP3-to-Text Using Openai Whisper (Works) SteveDigital Feb 18, 2023. 0) faster-whisper weight, whisper. It takes in raw audio recordings from many languages and outputs transcriptions in the language of origin or translated to english. co/openai/whisper-tiny with ONNX weights to be compatible with Transformers. I was wondering if I could potentially get your consultation on my notebook to see what i Feb 23, 2025 · Whisper是一种基于文本的预训练语言模型，由Meta Platforms（前Facebook AI）开发。如果你想下载Whisper模型，通常需要访问Hugging Face的Model Hub，这是一个提供各种开源机器学习模型的地方。 Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library). And the ASR system is composed of whisper encoder-decoder blocks: The pretrained whisper-large-v2 encoder is frozen. 2421; Wer: 17. The finetuning process took over 60 hours on dual Tesla A100 80Gb. Learn how to use Whisper with Hugging Face Transformers, Datasets and Accelerate libraries, and explore its features and performance. ct2-transformers-converter --model openai/whisper-large-v2 --output_dir faster-whisper-large-v2 \ --copy_files tokenizer. Conversion details Port of OpenAI's Whisper model in C/C++. Michael Osipov. Oct 10, 2024 · I am finetuning whisper v3 (learning_rate: 5e-6) with qlora (r=16) on medical data, both train loss and eval loss is decreasing pretty good(train_loss: 0. This makes it the fastest Whisper implementation available. [ ] Feb 26, 2025 · In my experience of using Whisper via OpenAI Azure services and comparing ASR performance (for same dataset) with Whisper large-v3 via HuggingFace. I have a Python script which uses the whisper. Whisper-Large-v3 是一个大型语言模型，适用于处理各种自然语言处理和文本生成任务。 Fine-tuned whisper-medium model for ASR in French This model is a fine-tuned version of openai/whisper-medium, trained on a composite dataset comprising of over 2200 hours of French speech audio, using the train and the validation splits of Common Voice 11. After preprocessing of the original dataset (all splits were mixed and splited to a new train + test split by 0. https://huggingface. The JAX implementation significantly enhances performance, running over 70x compared to the original Indic Whisper PyTorch code. Model details This model comes as a single checkpoint, whisper-v2-d3-e3. 1 Usage with faster whisper We also provide a converted model to be compatible with faster whisper. Alternatively, if you enter the huggingface repo id (e. Unlike models that output continuous embeddings, Ichigo Whisper compresses speech into discrete tokens, making it more compatible with Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. It is a model that compresses the Whipser Large model using knowledge distillation. flac audio2. Whisper-base-WebNN is meant to be used with the corresponding sample here for educational or testing purposes only. Nov 12, 2024. Note that you can use a fine-tuned Whisper model from HuggingFace or a local folder. Whisper Small Italian This model is a fine-tuned version of openai/whisper-small on the Common Voice 11. Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers I wanted to know that if I want to fine-tune for translation instead of transcription, I only need to change the task in the WhisperAudioProcessor and the metrics (using BLEU instead of WER) in the compute Whisper Finetune 1 Notebook In this experiment, Whisper (base) is finetuned on VinBigData 100h dataset, but with special pre-processing: Remove sentence with <unk> token (The data is clean and good compare to other open source Vietnamese data, but the transcript is the output of a larger model from Vinbigdata - Kaldi I think. However, the official Distil-Whisper checkpoints are English only, meaning they cannot be used for multilingual speech transcription. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. Model size FLEURS Whisper is a multi-lingual speech-to-text model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in the paper . Learn how to use Whisper with Hugging Face's WhisperProcessor and WhisperForConditionalGeneration classes. like 54. Note: Having a separate repo for ONNX weights is intended to be a Distil-Whisper is the perfect assistant model for English speech transcription, since it performs to within 1% WER of the original Whisper model, while being 6x faster over short and long-form audio samples. 0, Multilingual LibriSpeech, Voxpopuli, Fleurs, Multilingual TEDx, MediaSpeech, and African Accented French. 84 while the finetuned version shows 6. Unlike the original Whisper, which tends to omit disfluencies and follows more of a intended transcription style, CrisperWhisper aims to transcribe every spoken word exactly as it is It is used to instantiate a Whisper model according to the specified arguments, defining the model architecture. This model has been trained to predict casing, punctuation, and numbers. It achieves the following results on the evaluation set: eval_loss: 0. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. Sep 21, 2022 · Whisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. Open AI 推出的 Whisper是一个通用语音转录模型，在各种基准和音频条件下都取得了非常棒的结果。最新的 large-v3模型登顶了 OpenASR 排行榜，被评为最佳的开源英语语音转录模型。该模型在 Common Voice 15 数据集的 58 种语言中也展现 Whisper Hindi Large-v2 This model is a fine-tuned version of openai/whisper-large-v2 on the Hindi data available from multiple publicly available ASR corpuses. faster-whisper-small是OpenAI Whisper小型模型的优化版本，适用于CTranslate2框架。这个模型支持90多种语言的自动语音识别，采用float16量化以提高效率。开发者可通过faster-whisper库轻松集成该模型，适用于多种语音转文本场景。模型具有快速处理能力和广泛的语言覆盖范围，为自动语音识别任务提供了实用的 Construct a “fast” Whisper tokenizer (backed by HuggingFace’s tokenizers library). We’re on a journey to advance and democratize artificial intelligence through open source and open science. This approach will be faster than the openai-whisper package but with a higher VRAM consumption. Kotoba-Whisper-Bilingual is a collection of distilled Whisper models trained for. cpp development by creating an account on GitHub. This model can be used in CTranslate2 or projects based on CTranslate2 models such as faster-whisper. whisper_timestamped audio1. It is used to instantiate a Whisper model according to the specified arguments, defining the model architecture. Running 76. Whisper models for CTranslate2 with quantization INT8 This repository contains the conversion of OpenAI Whisper models to the CTranslate2 model format. The model is released as a part of Huggingface's Whisper fine-tuning event (December 2022). Please see this issue for more details and potential workarounds. Note: Having a separate repo for ONNX weights is intended to be 构建一个“快速”Whisper tokenizer (由 HuggingFace 的 tokenizers 库支持)。 此 tokenizer 继承自 PreTrainedTokenizerFast，其中包含大多数主要方法。用户应参考此超类以获取有关这些方法的更多信息。 Ichigo Whisper is a compact (22M parameters), open-source speech tokenizer for the Whisper-medium model, designed to enhance performance on multilingual with minimal impact on its original English capabilities. Oct 21, 2024 · We’re on a journey to advance and democratize artificial intelligence through open source and open science. js. Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. 0. However, due to the different implementation of the timestamp calculation in faster whisper or more precisely CTranslate2 the timestamp accuracy can not be guaranteed. We fine-tuned Whisper models for Thai using Commonvoice 13, Gowajee corpus , Thai Elderly Speech , Thai Dialect datasets. 95/0. candle-whisper. Discover amazing ML apps made by the community Spaces. It has been fine-tuned as a part of the Whisper fine-tuning sprint. Discover amazing ML apps made by the community Whisper large-v3 turbo model for CTranslate2 This repository contains the conversion of openai/whisper-large-v3-turbo to the CTranslate2 model format. 158 Mar 30, 2023 · I want to load this fine-tuned model using my existing Whisper installation. Whisper large-v3 has the same architecture as the previous large models except the following minor differences: The input uses 128 Mel frequency bins instead of 80 https://huggingface. srt file. 0 dataset. 3709; Model description More information needed. cpp weight. 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. Jan 1, 2025 · 以下の2つの話者ダイアライゼーション用の事前学習モデルを使用する。両方とも事前にHuggingFaceのサイト上で規約に同意しておく必要がある。 Sep 25, 2023 · Hi everyone, I’m working on WhisperModel. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Whisper-base-WebNN is an ONNX version of the Whisper-base model that optimizes for WebNN by using static input shapes and eliminates operators that are not in use. More details about it are available here. Intended uses & limitations More information needed Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. First make sure that you have a huggingface account and accept the licensing of the model. Whisper is a general-purpose speech recognition model that can perform multilingual speech recognition, speech translation, and language identification. Compared to the Whisper large model, the large-v2 model is trained for 2. The pretrained Whisper tokenizer is used. NB-Whisper is a cutting-edge series of models designed for automatic speech recognition (ASR) and speech translation. lmz / The performance of smaller Whisper model sizes on Swedish speech has also substantially improved, with kb-whisper-small outperforming openai/whisper-large-v3 (a model six times its size). Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Mar 21, 2024 · Distil-Whisper: distil-large-v3 Distil-Whisper was proposed in the paper Robust Knowledge Distillation via Large-Scale Pseudo Labelling. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. For instance, if you want to use the whisper-large-v2-nob model, you can simply do the following: whisper_timestamped --model NbAiLab/whisper-large-v2-nob <> Plot of word alignment Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. 39 (so far). g, deepdml/faster-whisper-large-v3-turbo-ct2) in the "Model" dropdown, it will be automatically downloaded in the directory. The figure below shows a WER breakdown by languages of Fleurs dataset, using the large model. It achieves the following results on the evaluation set: Loss: 0. whisper-v2-d3-e3 is a version of whisper-large-v2, fine-tuned by ivrit. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. Instantiating a configuration with the defaults will yield a similar configuration to that of the Whisper openai/whisper-tiny architecture. 2. 05, that is 225761/11883 rows respectively) the original Whisper v3 has WER 9. wav --model tiny --output_dir . I have seen that OpenAI Azure Whisper performs consistently better across different metrics like WER and even semantic similarity scores like BERTScore. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single processor. The obtained final acoustic representation is given to the greedy decoder. transcribe("audio. Anime Whisper 🤗🎤📝 Anime Whisper は、特に日本語のアニメ調演技セリフのドメインに特化した日本語音声認識モデルです。 このモデルは kotoba-whisper-v2. May 9, 2023 · According to fine-Tune Whisper For Multilingual ASR with 🤗 Transformers, I can fine-tine the Whisper model with languages supported by WhisperTokenizer. The original code repository can be found here. It is due to dependency conflicts between faster-whisper and pyannote-audio 3. 0 をベースモデルとして、約5,300時間373万ファイルのアニメ調の音声・台本データセット Galgame_Speech_ASR_16kHz でファインチューニングしたものです。 Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. CrisperWhisper is an advanced variant of OpenAI's Whisper, designed for fast, precise, and verbatim speech recognition with accurate (crisp) word-level timestamps. Users should refer to this superclass for more information regarding those methods. Training Details aiola/whisper-ner-v1 was trained on the NuNER dataset to perform joint audio transcription and NER tagging. NOTE: The code used to train this model is available for re-use in the whisper-finetune repository. mp3 audio3. nuc vwdyka yff lynkdvm hlhu jwttd uqpec gia gxo vgais cywz tiyxv ynvugq tuom lshvoi