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
File size: 983 Bytes
8d83dee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | #!/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))))
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