Automatic Speech Recognition
Transformers
Safetensors
English
Gujarati
wav2vec2
speech
audio
meta-asr
emotion-recognition
speaker-profiling
intent-detection
entity-extraction
ctc
gujarati
english
gujlish
code-switching
Eval Results (legacy)
Instructions to use WhissleAI/speech-tagger_gujlish_wav2vec2_meta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhissleAI/speech-tagger_gujlish_wav2vec2_meta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="WhissleAI/speech-tagger_gujlish_wav2vec2_meta")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("WhissleAI/speech-tagger_gujlish_wav2vec2_meta") model = AutoModelForCTC.from_pretrained("WhissleAI/speech-tagger_gujlish_wav2vec2_meta") - Notebooks
- Google Colab
- Kaggle
Upload model.safetensors with huggingface_hub
Browse files- model.safetensors +3 -0
model.safetensors
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