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64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
# Copyright (c) 2025, 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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"""Shared fakes for SALM / SALMAutomodel encoder-chunking tests."""
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from types import SimpleNamespace
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import torch
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def chunking_test_devices():
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devices = [torch.device("cpu")]
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if torch.cuda.is_available():
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devices.append(torch.device("cuda"))
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return devices
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class ChunkingTestTokenizer:
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pad = 0
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unk_id = None
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bos_id = 1
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eos_id = 2
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def __init__(self, audio_locator_tag):
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self._audio_locator_tag = audio_locator_tag
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def token_to_id(self, token):
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assert token == self._audio_locator_tag
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return 99
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class ChunkingTestPerception(torch.nn.Module):
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def __init__(self, sampling_rate, hop_length):
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super().__init__()
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self.preprocessor = SimpleNamespace(featurizer=_ChunkingTestFeaturizer(sampling_rate, hop_length))
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self.calls = []
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self.time_offsets = []
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self.spk_targets_calls = []
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def forward(self, input_signal=None, input_signal_length=None, time_offset=None, spk_targets=None):
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self.calls.append((input_signal.detach().clone(), input_signal_length.detach().clone()))
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self.time_offsets.append(None if time_offset is None else time_offset.detach().clone())
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self.spk_targets_calls.append(None if spk_targets is None else spk_targets.detach().clone())
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max_len = int(input_signal_length.max().item()) if input_signal_length.numel() > 0 else 0
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return input_signal[:, :max_len].unsqueeze(-1), input_signal_length.clone()
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class _ChunkingTestFeaturizer:
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def __init__(self, sampling_rate, hop_length):
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self.sample_rate = sampling_rate
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self.hop_length = hop_length
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def get_seq_len(self, seq_len):
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return torch.floor_divide(seq_len, self.hop_length).to(dtype=torch.long)
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