Instructions to use KevinGeng/Negel_152_AVA_script_conv_train_conv_dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevinGeng/Negel_152_AVA_script_conv_train_conv_dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KevinGeng/Negel_152_AVA_script_conv_train_conv_dev")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KevinGeng/Negel_152_AVA_script_conv_train_conv_dev") model = AutoModelForSpeechSeq2Seq.from_pretrained("KevinGeng/Negel_152_AVA_script_conv_train_conv_dev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9e56cdcd169c4202beb2e4bdb4ee6adc6e7d717adc285084c3fcb0e74bfd34d
- Size of remote file:
- 3.06 GB
- SHA256:
- f93445e560461230c80daafabe71176f2aaf849c8fa81b463c38eaddbf757705
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