Automatic Speech Recognition
Transformers
Safetensors
phoneticxeus
feature-extraction
phone-recognition
ipa
ctc
multilingual
xeus
custom_code
Instructions to use changelinglab/PhoneticXeus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use changelinglab/PhoneticXeus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="changelinglab/PhoneticXeus", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("changelinglab/PhoneticXeus", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add trust_remote_code support: AutoModel + safetensors + vendored src + beginner README
8d83dee verified | #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright 2019 Shigeki Karita | |
| # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| """Positionwise feed forward layer definition.""" | |
| import torch | |
| class PositionwiseFeedForward(torch.nn.Module): | |
| """Positionwise feed forward layer. | |
| Args: | |
| idim (int): Input dimenstion. | |
| hidden_units (int): The number of hidden units. | |
| dropout_rate (float): Dropout rate. | |
| """ | |
| def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()): | |
| """Construct an PositionwiseFeedForward object.""" | |
| super(PositionwiseFeedForward, self).__init__() | |
| self.w_1 = torch.nn.Linear(idim, hidden_units) | |
| self.w_2 = torch.nn.Linear(hidden_units, idim) | |
| self.dropout = torch.nn.Dropout(dropout_rate) | |
| self.activation = activation | |
| def forward(self, x): | |
| """Forward function.""" | |
| return self.w_2(self.dropout(self.activation(self.w_1(x)))) | |