Feature Extraction
Transformers
Safetensors
English
custom_model
multi-modal
speech-language
custom_code
Eval Results (legacy)
Instructions to use skit-ai/speechllm-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skit-ai/speechllm-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skit-ai/speechllm-2B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("skit-ai/speechllm-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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[The model is still training, we will be releasing the latest checkpoints soon...]
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SpeechLLM is a multi-modal LLM trained to predict the metadata of the speaker's turn in a conversation. speechllm-2B model is based on HubertX
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1. **SpeechActivity** : if the audio signal contains speech (True/False)
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2. **Transcript** : ASR transcript of the audio
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3. **Gender** of the speaker (Female/Male)
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[The model is still training, we will be releasing the latest checkpoints soon...]
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SpeechLLM is a multi-modal LLM trained to predict the metadata of the speaker's turn in a conversation. speechllm-2B model is based on HubertX audio encoder and TinyLlama LLM. The model predicts the following:
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1. **SpeechActivity** : if the audio signal contains speech (True/False)
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2. **Transcript** : ASR transcript of the audio
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3. **Gender** of the speaker (Female/Male)
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