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Fixing the pipeline tutorial test (#20746)

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Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Nicolas Patry
2022-12-13 19:08:30 +01:00
committed by GitHub
parent f28c918c7e
commit ba9da49aa2
1 changed files with 3 additions and 28 deletions
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31
docs/source/en/pipeline_tutorial.mdx
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@@ -133,30 +133,7 @@ For instance, the [`transformers.AutomaticSpeechRecognitionPipeline.__call__`] m
>>> # Not using whisper, as it cannot provide timestamps.
>>> generator = pipeline(model="facebook/wav2vec2-large-960h-lv60-self", return_timestamps="word")
>>> generator("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED',
'chunks': [
{'text': 'I', 'timestamp': (1.22, 1.24)},
{'text': 'HAVE', 'timestamp': (1.42, 1.58)},
{'text': 'A', 'timestamp': (1.66, 1.68)},
{'text': 'DREAM', 'timestamp': (1.76, 2.14)},
{'text': 'BUT', 'timestamp': (3.68, 3.8)},
{'text': 'ONE', 'timestamp': (3.94, 4.06)},
{'text': 'DAY', 'timestamp': (4.16, 4.3)},
{'text': 'THIS', 'timestamp': (6.36, 6.54)},
{'text': 'NATION', 'timestamp': (6.68, 7.1)},
{'text': 'WILL', 'timestamp': (7.32, 7.56)},
{'text': 'RISE', 'timestamp': (7.8, 8.26)},
{'text': 'UP', 'timestamp': (8.38, 8.48)},
{'text': 'AND', 'timestamp': (10.08, 10.18)},
{'text': 'LIVE', 'timestamp': (10.26, 10.48)},
{'text': 'OUT', 'timestamp': (10.58, 10.7)},
{'text': 'THE', 'timestamp': (10.82, 10.9)},
{'text': 'TRUE', 'timestamp': (10.98, 11.18)},
{'text': 'MEANING', 'timestamp': (11.26, 11.58)},
{'text': 'OF', 'timestamp': (11.66, 11.7)},
{'text': 'ITS', 'timestamp': (11.76, 11.88)},
{'text': 'CREED', 'timestamp': (12.0, 12.38)}
]}
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED', 'chunks': [{'text': 'I', 'timestamp': (1.22, 1.24)}, {'text': 'HAVE', 'timestamp': (1.42, 1.58)}, {'text': 'A', 'timestamp': (1.66, 1.68)}, {'text': 'DREAM', 'timestamp': (1.76, 2.14)}, {'text': 'BUT', 'timestamp': (3.68, 3.8)}, {'text': 'ONE', 'timestamp': (3.94, 4.06)}, {'text': 'DAY', 'timestamp': (4.16, 4.3)}, {'text': 'THIS', 'timestamp': (6.36, 6.54)}, {'text': 'NATION', 'timestamp': (6.68, 7.1)}, {'text': 'WILL', 'timestamp': (7.32, 7.56)}, {'text': 'RISE', 'timestamp': (7.8, 8.26)}, {'text': 'UP', 'timestamp': (8.38, 8.48)}, {'text': 'AND', 'timestamp': (10.08, 10.18)}, {'text': 'LIVE', 'timestamp': (10.26, 10.48)}, {'text': 'OUT', 'timestamp': (10.58, 10.7)}, {'text': 'THE', 'timestamp': (10.82, 10.9)}, {'text': 'TRUE', 'timestamp': (10.98, 11.18)}, {'text': 'MEANING', 'timestamp': (11.26, 11.58)}, {'text': 'OF', 'timestamp': (11.66, 11.7)}, {'text': 'ITS', 'timestamp': (11.76, 11.88)}, {'text': 'CREED', 'timestamp': (12.0, 12.38)}]}
```
As you can see, the model inferred the text and also outputted **when** the various words were pronounced
@@ -250,9 +227,7 @@ Using a [`pipeline`] for NLP tasks is practically identical.
... "I have a problem with my iphone that needs to be resolved asap!!",
... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
... )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!',
'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'],
'scores': [0.504,0.479,0.013,0.003,0.002]}
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}
```
### Multimodal pipeline
@@ -269,5 +244,5 @@ For example, if you use this [invoice image](https://huggingface.co/spaces/impir
... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
... question="What is the invoice number?",
... )
[{'score': 0.635722279548645, 'answer': '1110212019', 'start': 22, 'end': 22}]
[{'score': 0.42514941096305847, 'answer': 'us-001', 'start': 16, 'end': 16}]
```