90 lines
3.6 KiB
Python
90 lines
3.6 KiB
Python
# Copyright 2021 The HuggingFace Team. 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 unittest
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from transformers import AutoFeatureExtractor, AutoTokenizer, Speech2TextForConditionalGeneration, Wav2Vec2ForCTC
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from transformers.pipelines import AutomaticSpeechRecognitionPipeline
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from transformers.testing_utils import require_datasets, require_torch, require_torchaudio, slow
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# from .test_pipelines_common import CustomInputPipelineCommonMixin
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class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
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# pipeline_task = "automatic-speech-recognition"
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# small_models = ["facebook/s2t-small-mustc-en-fr-st"] # Models tested without the @slow decorator
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# large_models = [
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# "facebook/wav2vec2-base-960h",
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# "facebook/s2t-small-mustc-en-fr-st",
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# ] # Models tested with the @slow decorator
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@slow
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@require_torch
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@require_datasets
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def test_simple_wav2vec2(self):
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import numpy as np
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from datasets import load_dataset
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model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
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tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
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asr = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
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waveform = np.zeros((34000,))
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output = asr(waveform)
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self.assertEqual(output, {"text": ""})
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ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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filename = ds[0]["file"]
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output = asr(filename)
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self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
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filename = ds[0]["file"]
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with open(filename, "rb") as f:
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data = f.read()
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output = asr(data)
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self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
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@slow
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@require_torch
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@require_torchaudio
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@require_datasets
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def test_simple_s2t(self):
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import numpy as np
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from datasets import load_dataset
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model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-it-st")
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tokenizer = AutoTokenizer.from_pretrained("facebook/s2t-small-mustc-en-it-st")
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feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/s2t-small-mustc-en-it-st")
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asr = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
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waveform = np.zeros((34000,))
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output = asr(waveform)
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self.assertEqual(output, {"text": "E questo è il motivo per cui non ci siamo mai incontrati."})
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ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
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filename = ds[0]["file"]
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output = asr(filename)
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self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})
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filename = ds[0]["file"]
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with open(filename, "rb") as f:
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data = f.read()
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output = asr(data)
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self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})
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