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add code samples for TF speech models #16494

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Merged
ydshieh merged 1 commit into from
Apr 1, 2022
Merged

add code samples for TF speech models #16494

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63 changes: 63 additions & 0 deletions src/transformers/utils/doc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -794,6 +794,67 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
```
"""

TF_SPEECH_BASE_MODEL_SAMPLE = r"""
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from datasets import load_dataset

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = {processor_class}.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="tf")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
{expected_output}
```
"""

TF_SPEECH_CTC_SAMPLE = r"""
Example:

```python
>>> from transformers import {processor_class}, {model_class}
>>> from datasets import load_dataset
>>> import tensorflow as tf

>>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
>>> dataset = dataset.sort("id")
>>> sampling_rate = dataset.features["audio"].sampling_rate

>>> processor = {processor_class}.from_pretrained("{checkpoint}")
>>> model = {model_class}.from_pretrained("{checkpoint}")

>>> # audio file is decoded on the fly
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="tf")
>>> logits = model(**inputs).logits
>>> predicted_ids = tf.math.argmax(logits, axis=-1)

>>> # transcribe speech
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription[0]
{expected_output}
```

```python
>>> with processor.as_target_processor():
... inputs["labels"] = processor(dataset[0]["text"], return_tensors="tf").input_ids

>>> # compute loss
>>> loss = model(**inputs).loss
>>> round(float(loss), 2)
{expected_loss}
```
"""

TF_VISION_BASE_MODEL_SAMPLE = r"""
Example:

Expand Down Expand Up @@ -848,6 +909,8 @@ def _prepare_output_docstrings(output_type, config_class, min_indent=None):
"MaskedLM": TF_MASKED_LM_SAMPLE,
"LMHead": TF_CAUSAL_LM_SAMPLE,
"BaseModel": TF_BASE_MODEL_SAMPLE,
"SpeechBaseModel": TF_SPEECH_BASE_MODEL_SAMPLE,
"CTC": TF_SPEECH_CTC_SAMPLE,
"VisionBaseModel": TF_VISION_BASE_MODEL_SAMPLE,
"ImageClassification": TF_VISION_SEQ_CLASS_SAMPLE,
}
Expand Down