Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include ...
Cross-language transfer learning - Cross-language transfer learning is especially helpful when training new models for low-resource languages. But even when a substantial amount of data is available, cross-language transfer learning can help boost the performance further. It is based on the idea tha...
However, the benefits of such LARGE models are often limited to a few resource rich languages of the world. In this work, we make multiple contributions towards building ASR systems for low resource languages from the Indian subcontinent. First, we curate 17,000 hours of raw speech data for ...
Cross-language transfer learning is especially helpful when training new models for low-resource languages. But even when a substantial amount of data is available, cross-language transfer learning can help boost performance further. It is based on the idea that phoneme representation can be shared ...
Domain and Language Adaptation of Large-scale Pretrained Model for Speech Recognition of Low-resource Language The self-supervised learning (SSL) models are effective for automatic speech recognition (ASR). Due to the huge parameter size, it usually requires about 1... K Soky,LI Sheng,CC Kawahar...
Although these systems have achieved results, in languages building accurate and reliable ASR models for low resource languages like Gujarati comes with significant challenges. Gujarati lacks data and linguistic resources, making developing high-performance ASR systems quite difficult. In this paper, we ...
Nexa SDK is a comprehensive toolkit for supporting GGML and ONNX models. It supports text generation, image generation, vision-language models (VLM), Audio Language Model, auto-speech-recognition (ASR), and text-to-speech (TTS) capabilities. ...
python train.py --language Vietnamese --language_code vi --output_dir ../models/whisper-small-vi --save_to_hf In addition, thescriptsfolder will also contain Slurm batch scripts that we used to train the model on Yale High Performance Computing (HPC) clusters for references. ...
single trainable neural network model. E2E models require large amounts of paired speech text data that is expensive to obtain. The amount of data available varies across different languages and dialects. It is critical to make use of all these data so that both low resource languages and high...
(Note however, that other implementations can use one or more custom machine-trained models.) At least parts of the training-stage technique are also scalable because they can be used for any natural language. The language model produced by the training-stage technique is itself resource-...