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Make ASR pipeline compliant with Hub spec + add tests (#33769)

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* Remove max_new_tokens arg

* Add ASR pipeline to testing

* make fixup

* Factor the output test out into a util

* Full error reporting

* Full error reporting

* Update src/transformers/pipelines/automatic_speech_recognition.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* Small comment

---------

Co-authored-by: Lysandre Debut <hi@lysand.re>
This commit is contained in:
Matt
2024-10-01 18:15:04 +01:00
committed by GitHub
parent 0256520794
commit a43e84cb3b
5 changed files with 42 additions and 9 deletions
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6
tests/pipelines/test_pipelines_audio_classification.py
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@@ -13,7 +13,6 @@
# limitations under the License.
import unittest
from dataclasses import fields
import numpy as np
from huggingface_hub import AudioClassificationOutputElement
@@ -21,6 +20,7 @@ from huggingface_hub import AudioClassificationOutputElement
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
compare_pipeline_output_to_hub_spec,
is_pipeline_test,
nested_simplify,
require_tf,
@@ -68,10 +68,8 @@ class AudioClassificationPipelineTests(unittest.TestCase):
self.run_torchaudio(audio_classifier)
spec_output_keys = {field.name for field in fields(AudioClassificationOutputElement)}
for single_output in output:
output_keys = set(single_output.keys())
self.assertEqual(spec_output_keys, output_keys, msg="Pipeline output keys do not match HF Hub spec!")
compare_pipeline_output_to_hub_spec(single_output, AudioClassificationOutputElement)
@require_torchaudio
def run_torchaudio(self, audio_classifier):

5
tests/pipelines/test_pipelines_automatic_speech_recognition.py
View File

@@ -18,7 +18,7 @@ import unittest
import numpy as np
import pytest
from datasets import Audio, load_dataset
from huggingface_hub import hf_hub_download, snapshot_download
from huggingface_hub import AutomaticSpeechRecognitionOutput, hf_hub_download, snapshot_download
from transformers import (
MODEL_FOR_CTC_MAPPING,
@@ -36,6 +36,7 @@ from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
from transformers.pipelines.audio_utils import chunk_bytes_iter
from transformers.pipelines.automatic_speech_recognition import _find_timestamp_sequence, chunk_iter
from transformers.testing_utils import (
compare_pipeline_output_to_hub_spec,
is_pipeline_test,
is_torch_available,
nested_simplify,
@@ -86,6 +87,8 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
outputs = speech_recognizer(audio)
self.assertEqual(outputs, {"text": ANY(str)})
compare_pipeline_output_to_hub_spec(outputs, AutomaticSpeechRecognitionOutput)
# Striding
audio = {"raw": audio, "stride": (0, 4000), "sampling_rate": speech_recognizer.feature_extractor.sampling_rate}
if speech_recognizer.type == "ctc":