whisper_finetune_16bit

Model · Speech recognition

whisper-large-v3

OpenAI

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. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 The Whisper large-v3 model was trained on 1 million hours of weakly labeled audio and 4 million hours of pseudo-labeled audio collected using…

For usage instructions follow openai/whisper-large-v3. Large-v3 finetune trained as a baseline with smaller checkpoints in progress. Expecting worse long form and equal short form. Benchmarks. Has occasional repetition issue compared to previous models but achieves competitive/SOTA CER across all tested sets.

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

An ELARSpeech automatic speech recognition model for Bunak. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the ELARSpeech project…

An ELARSpeech automatic speech recognition model for Belait. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the ELARSpeech project…

An ELARSpeech automatic speech recognition model for Bastimentos Creole English. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the…