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66 lines
2.5 KiB
Python
66 lines
2.5 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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import pytest
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import torch
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from nemo.collections.asr.inference.streaming.buffering.audio_bufferer import AudioBufferer, BatchedAudioBufferer
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from nemo.collections.asr.inference.streaming.framing.mono_stream import MonoStream
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from nemo.collections.asr.inference.streaming.framing.multi_stream import MultiStream
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@pytest.fixture(scope="module")
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def test_audios():
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return torch.ones(83200), torch.ones(118960)
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class TestAudioBufferer:
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@pytest.mark.unit
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def test_audio_bufferer(self, test_audios):
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for audio in test_audios:
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stream = MonoStream(16000, frame_size_in_secs=2.5, stream_id=0, pad_last_frame=False)
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stream.load_audio(audio, options=None)
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frame_bufferer = AudioBufferer(16000, buffer_size_in_secs=5.0)
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for frame in iter(stream):
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frame = frame[0]
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frame_bufferer.update(frame)
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buffer = frame_bufferer.get_buffer()
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assert len(buffer) == frame_bufferer.buffer_size
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assert torch.allclose(buffer[-frame.size :], frame.samples, atol=1e-5)
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class TestBatchedAudioBufferer:
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@pytest.mark.unit
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def test_batched_audio_bufferer(self, test_audios):
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multi_stream = MultiStream(n_frames_per_stream=1)
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for stream_id, audio in enumerate(test_audios):
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stream = MonoStream(16000, 2.5, stream_id=stream_id, pad_last_frame=False)
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stream.load_audio(audio, options=None)
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multi_stream.add_stream(stream, stream_id=stream_id)
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batched_audio_bufferer = BatchedAudioBufferer(16000, buffer_size_in_secs=5.0)
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for frames in iter(multi_stream):
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buffered_frames, left_paddings = batched_audio_bufferer.update(frames)
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for idx, frame in enumerate(frames):
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frame_buffer = buffered_frames[idx]
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assert torch.allclose(frame_buffer[-frame.size :], frame.samples, atol=1e-5)
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