SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add SpeechEncoderDecoder & Speech2Text2 (#13186)

Browse Source 
* fix_torch_device_generate_test

* remove @

* up

* correct some bugs

* correct model

* finish speech2text extension

* up

* up

* up

* up

* Update utils/custom_init_isort.py

* up

* up

* update with tokenizer

* correct old tok

* correct old tok

* fix bug

* up

* up

* add more tests

* up

* fix docs

* up

* fix some more tests

* add better config

* correct some more things
"

* fix tests

* improve docs

* Apply suggestions from code review

* Apply suggestions from code review

* final fixes

* finalize

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* apply suggestions Lysandre and Sylvain

* apply nicos suggestions

* upload everything

* finish

Co-authored-by: Patrick von Platen <patrick@huggingface.co>
Co-authored-by: your_github_username <your_github_email>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Patrick von Platen
2021-09-01 13:33:31 +02:00
committed by GitHub
parent 9396b40433
commit 0b8c84e110
30 changed files with 3649 additions and 73 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tests/test_pipelines_audio_classification.py
View File

@@ -76,6 +76,8 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
@require_torch
def test_small_model_pt(self):
model = "anton-l/wav2vec2-random-tiny-classifier"
# hack: dummy tokenizer is required to prevent pipeline from failing
tokenizer = PreTrainedTokenizer()
audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
@@ -98,6 +100,8 @@ class AudioClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
import datasets
model = "superb/wav2vec2-base-superb-ks"
# hack: dummy tokenizer is required to prevent pipeline from failing
tokenizer = PreTrainedTokenizer()
audio_classifier = pipeline("audio-classification", model=model, tokenizer=tokenizer)
dataset = datasets.load_dataset("anton-l/superb_dummy", "ks", split="test")