Automatic Speech Recognition
Transformers
Safetensors
English
Italian
conformer_encoder_decoder
speech
speech recognition
speech translation
ASR
ST
custom_code
Instructions to use FBK-MT/fama-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FBK-MT/fama-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FBK-MT/fama-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForSpeechSeq2Seq model = AutoModelForSpeechSeq2Seq.from_pretrained("FBK-MT/fama-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 356fab6c011ec92bd3a8a842e94135819ddb2387c50dd86b4f1de793691a6330
- Size of remote file:
- 2.26 GB
- SHA256:
- 146a11810a1a6f5d1bc65b402e68ab9b4136605acb27e284e9b7fe238002af7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.