Feature Extraction
Transformers
Safetensors
English
custom_model
multi-modal
speech-language
custom_code
Eval Results (legacy)
Instructions to use skit-ai/speechllm-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skit-ai/speechllm-1.5B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skit-ai/speechllm-1.5B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("skit-ai/speechllm-1.5B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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language: en
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metrics:
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- type: wer
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value:
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name: Test WER
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- task:
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type: audio-classification
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| **librispeech-test-clean** | Read Speech | 11.51 | 0.9594 | | |
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| **librispeech-test-other** | Read Speech | 16.68 | 0.9297 | | |
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| **CommonVoice test** | Diverse Accent, Age |
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language: en
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metrics:
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- type: wer
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value: 26.02
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name: Test WER
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- task:
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type: audio-classification
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|:--------------------------:|:-------------------:|:-------------------:|:--------------:|:-----------:|:--------------:|
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| **librispeech-test-clean** | Read Speech | 11.51 | 0.9594 | | |
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| **librispeech-test-other** | Read Speech | 16.68 | 0.9297 | | |
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| **CommonVoice test** | Diverse Accent, Age | 26.02 | 0.9476 | 0.6498 | 0.8121 |
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