Pixel ML builds video intelligence for Southeast Asian media. These are the speech models we run for Southeast Asian media: captions, search, clips. Open weights, open data, runs on your GPU — or on a CPU. Google's EmbeddingGemma 2 audio encoder with a CTC head, trained by Pixel ML on openly licensed Indonesian speech (YODAS, FLEURS, Common Voice). An optional n-gram language model (folder lm/, 0.27 GB) built from Indonesian Wikipedia comes with the weights. Speed (one H100 80GB, side by side with Whisper on the same GPU type and container size; seaasr v0.2.1): greedy decoding is about 18x faster than batched Whisper large-v3 and 9x faster than large-v3-turbo on a 79-minute recording…
Open weights
apache-2.0
112M parameters
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli Abstract We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech…
Open weights
apache-2.0
94M parameters
transformers
J
Model · Speech recognition
JC
The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli Abstract We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech…
Open weights
apache-2.0
94M parameters
Model · Speech recognition
OpenAI
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. 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. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It…
Open weights
apache-2.0
73M parameters
transformers
Open weights
62M parameters
Open weights
62M parameters
