Instructions to use talkbank/CHATWhisper-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use talkbank/CHATWhisper-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="talkbank/CHATWhisper-en")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("talkbank/CHATWhisper-en") model = AutoModelForMultimodalLM.from_pretrained("talkbank/CHATWhisper-en") - Notebooks
- Google Colab
- Kaggle
TalkBank Batchalign CHATWhisper
CHATWhisper is a series of ASR models specifically designed for the task for Language Sample Analysis (LSA) released by the TalkBank project, which delivers superior performance in the analysis of conversational speech transcripts, especially with regards to the analysis of filled pauses, retraicings, and stuttering.
The models are based on openai/whisper-large-v2 trained using an alpha=32, rank=16 LoRA. We will update the model card with evaluation performance shortly.
Usage
The models can be used directly as a Whisper-class ASR model following the same instructions released by OpenAI. Alternatively, to get the full analysis possible with the model, it is best combined with the TalkBank Batchalign suite of analysis software, available here, using transcribe mode with the --whisper flag.
Data
The models are trained with a combination of English Control Protocol samples from the AphasiaBank corpus of conversational speech from three seperate corpora.
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