Instructions to use facebook/w2v-bert-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/w2v-bert-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="facebook/w2v-bert-2.0")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("facebook/w2v-bert-2.0") model = AutoModel.from_pretrained("facebook/w2v-bert-2.0") - Notebooks
- Google Colab
- Kaggle
Update README.md
#5
by longnv - opened
README.md
CHANGED
|
@@ -119,6 +119,7 @@ import torch
|
|
| 119 |
from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
|
| 120 |
from fairseq2.memory import MemoryBlock
|
| 121 |
from fairseq2.nn.padding import get_seqs_and_padding_mask
|
|
|
|
| 122 |
from pathlib import Path
|
| 123 |
from seamless_communication.models.conformer_shaw import load_conformer_shaw_model
|
| 124 |
|
|
|
|
| 119 |
from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
|
| 120 |
from fairseq2.memory import MemoryBlock
|
| 121 |
from fairseq2.nn.padding import get_seqs_and_padding_mask
|
| 122 |
+
from fairseq2.data import Collater
|
| 123 |
from pathlib import Path
|
| 124 |
from seamless_communication.models.conformer_shaw import load_conformer_shaw_model
|
| 125 |
|