Audio Classification
Transformers
Safetensors
whisper
telephony
answering-machine-detection
amd
speech-processing
real-time
Generated from Trainer
Eval Results (legacy)
Instructions to use AbijahKaj/whisper-telephony-amd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbijahKaj/whisper-telephony-amd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="AbijahKaj/whisper-telephony-amd")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("AbijahKaj/whisper-telephony-amd") model = AutoModelForAudioClassification.from_pretrained("AbijahKaj/whisper-telephony-amd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AbijahKaj/whisper-telephony-amd: direct link, hf CLI and curl.
- Browser
- Download file 5.27 kB
-
https://huggingface.co/AbijahKaj/whisper-telephony-amd/resolve/main/training_args.bin
- Command line
-
hf download hf://AbijahKaj/whisper-telephony-amd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AbijahKaj/whisper-telephony-amd/resolve/main/training_args.bin
5.27 kB
- Xet hash:
- 8ee7811029e6d1276107ed3f505febf8c380bcdd6179fbd5b29ff847e750a669
- Size of remote file:
- 5.27 kB
- SHA256:
- 749eef72ebc0dc5ffecc6c394c953d5d4de85bd61492aa7ad6134838223e800b
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