Instructions to use babusza/typhoon-whisper-v5-ark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use babusza/typhoon-whisper-v5-ark with PEFT:
Task type is invalid.
- Transformers
How to use babusza/typhoon-whisper-v5-ark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="babusza/typhoon-whisper-v5-ark")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("babusza/typhoon-whisper-v5-ark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
- typhoon-whisper-v5-ark
- Model Details
- Uses
- Bias, Risks, and Limitations
- How to Get Started with the Model
- Training Details
- Evaluation
- Model Examination [optional]
- Environmental Impact
- Technical Specifications [optional]
- Citation [optional]
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
typhoon-whisper-v5-ark
Fine-tuned Whisper LoRA adapter for Thai insurance call center speech recognition.
Model Details
Model Description
typhoon-whisper-v5-ark is a LoRA adapter fine-tuned on top of Whisper large-v3,
optimized for Thai language automatic speech recognition (ASR) in insurance call center
domain (Chubb × TTB). The model is trained to accurately transcribe:
Thai personal names and ID numbers
Insurance-specific terminology
Consent and agreement phrases
Mixed Thai-English speech
Developed by: ARK Insights Co., Ltd.
Model type: LoRA Adapter (PEFT) on Whisper large-v3
Language(s): Thai (th)
License: Apache 2.0
Base model: openai/whisper-large-v3
Fine-tuning framework: PEFT 0.19.1 + HuggingFace Transformers
Model Sources
Uses
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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Framework versions
- PEFT 0.19.1
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Model tree for babusza/typhoon-whisper-v5-ark
Base model
openai/whisper-large-v3