![]() Select the timestamp of any transcript section to play that portion of audio. The relevant transcript section highlights as it plays. Use the controls at the top of the Transcribe pane to play back your audio. The audio file, whether recorded or uploaded, is saved to the Transcribed Files folder in OneDrive. You can interact with the transcript in a few different ways. If you close and reopen the pane or close and reopen the document, the transcript remains saved with the document. Your transcript is associated with the document it’s attached to until you remove it. Feel free to do other work or switch browser tabs or applications and come back later. Keep the Transcribe pane open while the transcription is being made. Transcription may take a while depending on your internet speed. When finished, select Save and transcribe now to save your recording to OneDrive and start the transcription process. Resume recording by selecting the microphone icon. Pause recording by selecting the pause icon. Leave the Transcribe pane open while recording. Start talking or begin a conversation with another person. ![]() Wait for the pause icon to be outlined in blue and the timestamp to start incrementing to let you know that recording has begun. That way, the recording can pick up the sound coming out of your device. If you want to record and transcribe a virtual call, don't use your headset. For example, if your computer's microphone input is set to your headset mic based on the last time you used it, it won't work well for picking up an in-person meeting. Both are found in the paper.Be careful to set the correct microphone input on your device, otherwise results may be disappointing. Meanwhile, more BLEU (Bilingual Evaluation Understudy) scores can be found in Appendix D.3. Additional WER scores corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4. The figure below shows a WER (Word Error Rate) breakdown by languages of the Fleurs dataset using the large-v2 model (The smaller the numbers, the better the performance). 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 for English-only applications 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. Pip install setuptools-rust Available models and languages You can download and install (or update to) the latest release of Whisper with the following command: The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. ![]() 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 3.8-3.11 and recent PyTorch versions. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many 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 multitasking model that can perform multilingual speech recognition, speech translation, and language identification. Whisper is a general-purpose speech recognition model.
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