fine-tuning

A python implementation of “Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer” [TASLP 2024]

Training LLaMA language model with MMEngine! It supports LoRA fine-tuning!

Using deep research workflow to generate datasets for finetuning LLMs.

Run, quantize, and fine-tune LLMs on Apple Silicon. Pure Rust, no Python, no CUDA, no ONNX

Finetune BLOOM

Resource for "A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective"

This is a obsidian plugin to help with the creation of personal jsonl datasets for text generation models.

Save, load, host, and share AI model checkpoints without slowing down training. Host on Lightning AI or your own cloud with enterprise-grade access controls.

Preprint: Asymmetry in Low-Rank Adapters of Foundation Models

Zero-shot voice cloning text-to-speech (TTS) with explicit emotion class conditioning built on F5-TTS

Fine tuning of the base model from OpenAI Whisper in Russian language on the dataset Sber-golos

SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models

[InterSpeech 2024] Official code repository of paper titled "Bird Whisperer: Leveraging Large Pre-trained Acoustic Model for Bird Call Classification" accepted in InterSpeech 2024 conference.

real time japanese speech recognition translator using wav2vec2

Synthetic medical VQA pipeline: 119K images annotated by frontier VLMs, cross-validated at 93% agreement, fine-tuned on 3 model families (2-3B params)

Train YOLO + VLM with one command. Auto-generate vision-language training data from YOLO labels - no extra labeling needed.

Developed a custom application of the Segment Anything Model (SAM) for breast cancer tissue segmentation, utilizing Hugging Face's Transformers and fine-tuning the decoder to predict segmentation masks in medical imaging.

Personal Identifiable Information (PII) entity detection and performance enhancement with synthetic data generation

Simple and lightweight tool to fine-tune GPT models (like GPT-2 and GPT-Neo) using your own data — built with Python and Transformers. Adapt powerful language models to your domain with ease.

This comprehensive guide provides a universal process for preparing your own speech datasets and training a custom Text-to-Speech (TTS) model.