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[Speech] Move all examples to new audio feature (#14045)

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This commit is contained in:
Patrick von Platen
2021-10-18 12:52:40 +02:00
committed by GitHub
parent 4334095c32
commit bdf31d6e0a
11 changed files with 43 additions and 108 deletions
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2
examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
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@@ -342,7 +342,7 @@ def main():
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f"--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. "
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
"Make sure to set `--audio_column_name` to the correct audio column - one of "
f"{', '.join(raw_datasets['train'].column_names)}."
)

4
setup.py
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@@ -136,7 +136,7 @@ _deps = [
"scikit-learn",
"sentencepiece>=0.1.91,!=0.1.92",
"sigopt",
"soundfile",
"librosa",
"sphinx-copybutton",
"sphinx-markdown-tables",
"sphinx-rtd-theme==0.4.3", # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
@@ -254,7 +254,7 @@ extras["sigopt"] = deps_list("sigopt")
extras["integrations"] = extras["optuna"] + extras["ray"] + extras["sigopt"]
extras["serving"] = deps_list("pydantic", "uvicorn", "fastapi", "starlette")
extras["audio"] = deps_list("soundfile")
extras["audio"] = deps_list("librosa")
extras["speech"] = deps_list("torchaudio") + extras["audio"] # `pip install ".[speech]"` is deprecated and `pip install ".[torch-speech]"` should be used instead
extras["torch-speech"] = deps_list("torchaudio") + extras["audio"]
extras["tf-speech"] = extras["audio"]

2
src/transformers/dependency_versions_table.py
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@@ -54,7 +54,7 @@ deps = {
"scikit-learn": "scikit-learn",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"sigopt": "sigopt",
"soundfile": "soundfile",
"librosa": "librosa",
"sphinx-copybutton": "sphinx-copybutton",
"sphinx-markdown-tables": "sphinx-markdown-tables",
"sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",

18
tests/test_modeling_flax_wav2vec2.py
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@@ -356,21 +356,13 @@ class FlaxWav2Vec2ModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_inference_ctc_robust_batched(self):
model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", from_pt=True)

16
tests/test_modeling_hubert.py
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@@ -613,21 +613,11 @@ class HubertModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
from datasets import load_dataset

20
tests/test_modeling_sew.py
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@@ -407,21 +407,13 @@ class SEWModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_inference_pretrained_batched(self):
model = SEWModel.from_pretrained("asapp/sew-tiny-100k").to(torch_device)

20
tests/test_modeling_sew_d.py
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@@ -428,21 +428,13 @@ class SEWDModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_inference_pretrained_batched(self):
model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k").to(torch_device)

13
tests/test_modeling_speech_to_text.py
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@@ -715,18 +715,11 @@ class Speech2TextModelIntegrationTests(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.sort("id").select(range(num_samples)).map(map_to_array)
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_generation_librispeech(self):
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr")

18
tests/test_modeling_tf_hubert.py
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@@ -479,21 +479,13 @@ class TFHubertModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_inference_ctc_normal(self):
model = TFHubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")

18
tests/test_modeling_tf_wav2vec2.py
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@@ -479,21 +479,13 @@ class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def test_inference_ctc_normal(self):
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

18
tests/test_modeling_wav2vec2.py
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@@ -900,21 +900,13 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
from datasets import load_dataset
import soundfile as sf
ids = [f"1272-141231-000{i}" for i in range(num_samples)]
# map files to raw
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
ds = ds.filter(lambda x: x["id"] in ids).sort("id").map(map_to_array)
return ds["speech"][:num_samples]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
from datasets import load_dataset