mms-lid-256

Model · Audio classification

mms-lid-126

AI at Meta

This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 126 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 126 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 126 languages. Let's look at a simple example. First, we install transformers and some other libraries Note: In order to use MMS you need to have…

Model · Audio classification

mms-lid-512

AI at Meta

This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 512 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 512 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 512 languages. Let's look at a simple example. First, we install transformers and some other libraries Note: In order to use MMS you need to have…

Model · Audio classification

mms-lid-1024

AI at Meta

This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 1024 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 1024 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 1024 languages. Let's look at a simple example. First, we install transformers and some other libraries Note: In order to use MMS you need to…

Model · Audio classification

mms-lid-4017

AI at Meta

This checkpoint is a model fine-tuned for speech language identification (LID) and part of Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and classifies raw audio input to a probability distribution over 4017 output classes (each class representing a language). The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on 4017 languages. This MMS checkpoint can be used with Transformers to identify the spoken language of an audio. It can recognize the following 4017 languages. Let's look at a simple example. First, we install transformers and some other libraries Note: In order to use MMS you need to…

Model · Audio classification

speech-emotion-recognition-with-openai-whisper-large-v3

Muhammad Firdho

This project leverages the Whisper model to recognize emotions in speech. The goal is to classify audio recordings into different emotional categories, such as Happy, Sad, Surprised, and etc. The dataset used for training and evaluation is sourced from multiple datasets, including: The dataset contains recordings labeled with various emotions. Below is the distribution of the emotions in the dataset: This distribution reflects the balance of emotions in the dataset, with some emotions having more samples than others. Excluded the "calm" emotion during training due to its underrepresentation. The model used is the Whisper Large V3 model, fine-tuned for audio classification tasks: I map the…

Model · Audio classification

Qwen3-ForcedAligner-0.6B-4bit

Ivan

4-bit quantized version of Qwen/Qwen3-ForcedAligner-0.6B for Apple Silicon inference via MLX. Predicts word-level timestamps for audio+text pairs in a single non-autoregressive forward pass. Unlike ASR (autoregressive, token-by-token), the forced aligner runs the entire sequence in one forward pass through the decoder. The classify head predicts a timestamp class (0–4999) at each token position, which maps to time via classindex × 80ms. This model is designed for use with speech-swift: Text decoder (attention projections, MLP, embeddings) quantized to 4-bit using group quantization (groupsize=64). Audio encoder and classify head kept as float16 for accuracy.