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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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Standalone ECAPA-TDNN voice encoder extracted from Qwen/Qwen3-TTS-12Hz-1.7B-Base. Produces **2048-dimensional** x-vector speaker embeddings from audio. The encoder follows the ECAPA-TDNN architecture (Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification) and uses Res2Net blocks, squeeze-excitation attention, and attentive statistical pooling. > [!WARNING] > **Only extracted from the `*-Base` model variant.** The `*-CustomVoice` and `*-VoiceDesign` variants do **NOT** support speaker embeddings and will not work. Do not attempt to use them for speaker encoding.

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> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - **Repository:** https://github.com/McGill-NLP/llm2vec - **Paper:** https://arxiv.org/abs/2404.05961 If you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`).

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> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - **Repository:** https://github.com/McGill-NLP/llm2vec - **Paper:** https://arxiv.org/abs/2404.05961 If you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`).

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> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - **Repository:** https://github.com/McGill-NLP/llm2vec - **Paper:** https://arxiv.org/abs/2404.05961 If you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`).

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> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance. - **Repository:** https://github.com/McGill-NLP/llm2vec - **Paper:** https://arxiv.org/abs/2404.05961 If you have any question about the code, feel free to email Parishad (`parishad.behnamghader@mila.quebec`) and Vaibhav (`vaibhav.adlakha@mila.quebec`).

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This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD\_Japanese\_BCCWJ r2.8. The entire spaCy v3 model is distributed as a python package named `ja_ginza_electra` from PyPI along with `GiNZA v5` which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures. Try running it as below: This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo.

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You can experience **speech emotion recognition** by selecting Gender/Speech recognition tab via our interactive Hugging Face Space: Upload an audio clip or record your voice to visualize categorical emotions and dimensional affect trajectories in real time. **MERaLiON-SER-v1** is a multilingual **speech emotion recognition (SER)** model jointly predicting 1. **Categorical emotions** – 7 discrete classes *(Neutral, Happy, Sad, Angry, Surprised, Fearful, Disgusted)*, and 2. **Dimensional affect values** – continuous *(Valence, Arousal, Dominance)*. Valence (0 negative, 1 positive), Arousal (0 calm, 1 active), Dominance (0 weak, 1 strong)

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We introduce **MERaLiON-SpeechEncoder-2**, our next-generation multilingual speech encoder that was pre-trained from scratch on a greatly expanded corpus of **1.4 million hours** of unlabeled audio, with a **strong focus on Southeast Asian (SEA) languages and accents**. As a speech foundation model, it encodes speech into a general-purpose, multilingual acoustic representation that can serve as a high-performance backbone for a wide range of downstream tasks — including automatic speech recognition (ASR), speech translation, speaker and language identification, and emotion recognition. **This model can be finetuned on custom datasets, allowing developers to build speech systems tailored to their specific needs.** Unlike many existing models optimized for high-resource, Western languages, MERaLiON-SpeechEncoder-2 is designed from the ground up to reflect the linguistic diversity and complexity of Southeast Asia. Our training data was curated to contain a substantial amount originating from Singapore and SEA, including 60,000 hours of Singapore-accented speech, with a further 160,000 hours covering Singapore’s official languages Chinese, Malay and Tamil, along with a smaller portion of dialects like Hokkien and Cantonese. SEA data amounts to 200,000 hours, including significant proportions of Malay, Thai, Indonesian, Vietnamese, with smaller amounts of Tagalog, Burmese, Javanese, Sundanese, Khmer and Lao. See below for a regional breakdown of the language coverage of our pre-training data. With only **630M parameters (≈2.5 GB in memory)**, the model is easily deployable on most commercial GPUs, eliminating the need for distributed or large-scale compute setups.

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More details about the infinity inference project please refer to the Github: Infinity. To deploy files with the infinity_emb pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Michael Feil (infinity at michaelfeil.eu).

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