mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-18 07:41:03 +00:00
Brandon Thomas
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from abc import ABC, abstractmethod
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from typing import List, Optional, Union
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import torch
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from omegaconf import OmegaConf
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from nemo.core.classes import ModelPT
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from nemo.core.classes.exportable import Exportable
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from nemo.utils import model_utils
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__all__ = ['ASRModel']
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class ASRModel(ModelPT, ABC):
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@abstractmethod
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def transcribe(self, paths2audio_files: List[str], batch_size: int = 4) -> List[str]:
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"""
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Takes paths to audio files and returns text transcription
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Args:
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paths2audio_files: paths to audio fragment to be transcribed
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Returns:
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transcription texts
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"""
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pass
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def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
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val_loss_mean = torch.stack([x['val_loss'] for x in outputs]).mean()
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wer_num = torch.stack([x['val_wer_num'] for x in outputs]).sum()
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wer_denom = torch.stack([x['val_wer_denom'] for x in outputs]).sum()
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tensorboard_logs = {'val_loss': val_loss_mean, 'val_wer': wer_num / wer_denom}
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return {'val_loss': val_loss_mean, 'log': tensorboard_logs}
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def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
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val_loss_mean = torch.stack([x['test_loss'] for x in outputs]).mean()
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wer_num = torch.stack([x['test_wer_num'] for x in outputs]).sum()
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wer_denom = torch.stack([x['test_wer_denom'] for x in outputs]).sum()
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tensorboard_logs = {'test_loss': val_loss_mean, 'test_wer': wer_num / wer_denom}
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return {'test_loss': val_loss_mean, 'log': tensorboard_logs}
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@classmethod
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def list_available_models(cls) -> 'List[PretrainedModelInfo]':
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"""
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This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
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Returns:
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List of available pre-trained models.
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"""
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# recursively walk the subclasses to generate pretrained model info
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list_of_models = model_utils.resolve_subclass_pretrained_model_info(cls)
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return list_of_models
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class ExportableEncDecModel(Exportable):
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"""
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Simple utiliy mix-in to export models that consist of encoder/decoder pair
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plus pre/post processor, but have to be exported as encoder/decoder pair only
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(covers most ASR classes)
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"""
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@property
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def input_module(self):
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return self.encoder
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@property
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def output_module(self):
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return self.decoder
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def forward_for_export(self, input, length=None):
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encoder_output = self.input_module(input, length)
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if isinstance(encoder_output, tuple):
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return self.output_module(encoder_output[0])
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else:
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return self.output_module(encoder_output)
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def _prepare_for_export(self, **kwargs):
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self.input_module._prepare_for_export(**kwargs)
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self.output_module._prepare_for_export(**kwargs)
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