12/16/2023 0 Comments Phoenix viewer whisperThe code and the model weights of Whisper are released under the MIT License. Please use the □ Show and tell category in Discussions for sharing more example usages of Whisper and third-party extensions such as web demos, integrations with other tools, ports for different platforms, etc. decode ( model, mel, options ) # print the recognized text print ( result. detect_language ( mel ) print ( f "Detected language: " ) # decode the audio options = whisper. device ) # detect the spoken language _, probs = model. pad_or_trim ( audio ) # make log-Mel spectrogram and move to the same device as the model mel = whisper. load_audio ( "audio.mp3" ) audio = whisper. load_model ( "base" ) # load audio and pad/trim it to fit 30 seconds audio = whisper. 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.īelow is an example usage of tect_language() and code() which provide lower-level access to the model. transcribe ( "audio.mp3" ) print ( result ) Transcription can also be performed within Python: import whisper model = whisper. See tokenizer.py for the list of all available languages. Run the following to view all available options: whisper -help To transcribe an audio file containing non-English speech, you can specify the language using the -language option: whisper japanese.wav -language JapaneseĪdding -task translate will translate the speech into English: whisper japanese.wav -language Japanese -task translate The default setting (which selects the small model) works well for transcribing English. The following command will transcribe speech in audio files, using the medium model: whisper audio.flac audio.mp3 audio.wav -model medium More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D in the paper. The figure below shows a WER (Word Error Rate) breakdown by languages of Fleurs dataset, using the large-v2 model. Whisper's performance varies widely depending on the language. We observed that the difference becomes less significant for the small.en and medium.en models. en models tend to perform better, especially for the tiny.en and base.en models. Below are the names of the available models and their approximate memory requirements and relative speed. There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. If the installation fails with No module named 'setuptools_rust', you need to install setuptools_rust, e.g. ![]() Additionally, you may need to configure the PATH environment variable, e.g. If you see installation errors during the pip install command above, please follow the Getting started page to install Rust development environment. You may need rust installed as well, in case tokenizers does not provide a pre-built wheel for your platform. Sudo apt update & sudo apt install ffmpeg It also requires the command-line tool ffmpeg to be installed on your system, which is available from most package managers: # on Ubuntu or Debian To update the package to the latest version of this repository, please run: pip install -upgrade -no-deps -force-reinstall git+ You can download and install (or update to) the latest release of Whisper with the following command: pip install -U openai-whisperĪlternatively, the following command will pull and install the latest commit from this repository, along with its Python dependencies: pip install git+ ![]() The codebase also depends on a few Python packages, most notably HuggingFace Transformers for their fast tokenizer implementation and ffmpeg-python for reading audio files. We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. ![]() All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. ApproachĪ Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. Whisper is a general-purpose speech recognition model.
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