Instructions to use m-a-p/MERT-v1-330M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use m-a-p/MERT-v1-330M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="m-a-p/MERT-v1-330M", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("m-a-p/MERT-v1-330M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#13 opened about 1 year ago
by
SFconvertbot
License
👍 2
#12 opened about 1 year ago
by
mrfakename
Adding `safetensors` variant of this model
#11 opened about 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#10 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#8 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#7 opened about 2 years ago
by
SFconvertbot
passing device_map argument breaks the model
#6 opened over 2 years ago
by
x75
Are there any plans to distribute the checkpoint files trained on each downstream task?
1
#5 opened over 2 years ago
by
itsuki1021
Confusion about the use of the Encodec model
1
#4 opened over 2 years ago
by
xtluo
can you provide fairseq checkpoint?
2
#1 opened about 3 years ago
by
lzl1456