Wav2Vec2 base model trained of 3K hours of Vietnamese speech
The base model is pre-trained on 16kHz sampled speech audio from Vietnamese speech corpus containing 3K hours of spontaneous, reading, and broadcasting speech. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Vietnamese Automatic Speech Recognition.
Note
: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out
this blog
for more in-detail explanation of how to fine-tune the model.
Facebook's Wav2Vec2 blog
Paper
Usage
See
this notebook
for more information on how to fine-tune the English pre-trained model.
import torch
from transformers import Wav2Vec2Model
model = Wav2Vec2Model.from_pretrained("dragonSwing/viwav2vec2-base-3k")
# Sanity check
inputs = torch.rand([1, 16000])
outputs = model(inputs)
Runs of dragonSwing viwav2vec2-base-3k on huggingface.co
11
Total runs
0
24-hour runs
-1
3-day runs
-1
7-day runs
1
30-day runs
More Information About viwav2vec2-base-3k huggingface.co Model
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dragonSwing viwav2vec2-base-3k online free url in huggingface.co:
viwav2vec2-base-3k is an open source model from GitHub that offers a free installation service, and any user can find viwav2vec2-base-3k on GitHub to install. At the same time, huggingface.co provides the effect of viwav2vec2-base-3k install, users can directly use viwav2vec2-base-3k installed effect in huggingface.co for debugging and trial. It also supports api for free installation.