source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#evaluate | .md | >>> accuracy = evaluate.load("accuracy")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the accuracy:
```py
>>> import numpy as np | 97_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#evaluate | .md | >>> def compute_metrics(eval_pred):
... predictions = np.argmax(eval_pred.predictions, axis=1)
... return accuracy.compute(predictions=predictions, references=eval_pred.label_ids)
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training. | 97_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | <frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load Wav2Vec2 with [`AutoModelForAudioClassification`] along with the number of... | 97_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | >>> num_labels = len(id2label)
>>> model = AutoModelForAudioClassification.from_pretrained(
... "facebook/wav2vec2-base", num_labels=num_labels, label2id=label2id, id2label=id2label
... )
```
At this point, only three steps remain: | 97_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | ... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir`, which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to uplo... | 97_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to fine-tune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_mind_model",
... eval_strategy="epoch",
... sav... | 97_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | ... per_device_train_batch_size=32,
... gradient_accumulation_steps=4,
... per_device_eval_batch_size=32,
... num_train_epochs=10,
... warmup_ratio=0.1,
... logging_steps=10,
... load_best_model_at_end=True,
... metric_for_best_model="accuracy",
... push_to_hub=True,
... ) | 97_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | >>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=encoded_minds["train"],
... eval_dataset=encoded_minds["test"],
... processing_class=feature_extractor,
... compute_metrics=compute_metrics,
... ) | 97_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#train | .md | >>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
For a more in-depth example of how to fine-tune a model for audio classificati... | 97_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | Great, now that you've fine-tuned a model, you can use it for inference!
Load an audio file for inference. Remember to resample the sampling rate of the audio file to match the model's sampling rate, if necessary.
```py
>>> from datasets import load_dataset, Audio | 97_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | >>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> audio_file = dataset[0]["audio"]["path"]
```
The simplest way to try out your fine-tuned model for inference ... | 97_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | >>> classifier = pipeline("audio-classification", model="stevhliu/my_awesome_minds_model")
>>> classifier(audio_file)
[
{'score': 0.09766869246959686, 'label': 'cash_deposit'},
{'score': 0.07998877018690109, 'label': 'app_error'},
{'score': 0.0781070664525032, 'label': 'joint_account'},
{'score': 0.07667109370231628, '... | 97_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | ]
```
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Load a feature extractor to preprocess the audio file and return the `input` as PyTorch tensors:
```py
>>> from transformers import AutoFeatureExtractor | 97_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | >>> feature_extractor = AutoFeatureExtractor.from_pretrained("stevhliu/my_awesome_minds_model")
>>> inputs = feature_extractor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
```
Pass your inputs to the model and return the logits:
```py
>>> from transformers import AutoModelForAudio... | 97_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/audio_classification.md | https://huggingface.co/docs/transformers/en/tasks/audio_classification/#inference | .md | >>> model = AutoModelForAudioClassification.from_pretrained("stevhliu/my_awesome_minds_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the class with the highest probability, and use the model's `id2label` mapping to convert it to a label:
```py
>>> import torch
>>> predicted_clas... | 97_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 98_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 98_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#automatic-speech-recognition | .md | [[open-in-colab]]
<Youtube id="TksaY_FDgnk"/>
Automatic speech recognition (ASR) converts a speech signal to text, mapping a sequence of audio inputs to text outputs. Virtual assistants like Siri and Alexa use ASR models to help users every day, and there are many other useful user-facing applications like live cap... | 98_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#automatic-speech-recognition | .md | This guide will show you how to:
1. Fine-tune [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) on the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset to transcribe audio to text.
2. Use your fine-tuned model for inference.
<Tip>
To see all architectures and checkpoints compatible with ... | 98_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#automatic-speech-recognition | .md | </Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate jiwer
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> fr... | 98_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#automatic-speech-recognition | .md | >>> notebook_login()
``` | 98_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | Start by loading a smaller subset of the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset from the 🤗 Datasets library. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset, Audio | 98_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | >>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
```
Split the dataset's `train` split into a train and test set with the [`~Dataset.train_test_split`] method:
```py
>>> minds = minds.train_test_split(test_size=0.2)
```
Then take a look at the dataset:
```py
>>> minds
DatasetDict({
... | 98_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | num_rows: 16
})
test: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 4
})
})
```
While the dataset contains a lot of useful information, like `lang_id` and `english_transcription`, this guide focuses on the `audio` and `transcription`. Remove the ... | 98_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | ```py
>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
```
Review the example again:
```py
>>> minds["train"][0]
{'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414,
0.00024414, 0.00024414], dtype=float32),
'path': '/root/.cache/huggingface/datas... | 98_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | 'sampling_rate': 8000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance ... | 98_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#load-minds-14-dataset | .md | - `audio`: a 1-dimensional `array` of the speech signal that must be called to load and resample the audio file.
- `transcription`: the target text. | 98_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | The next step is to load a Wav2Vec2 processor to process the audio signal:
```py
>>> from transformers import AutoProcessor | 98_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
```
The MInDS-14 dataset has a sampling rate of 8000Hz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000Hz to use the pretrained Wav2Vec... | 98_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | >>> minds["train"][0]
{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b... | 98_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | 'sampling_rate': 16000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance... | 98_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ```
As you can see in the `transcription` above, the text contains a mix of uppercase and lowercase characters. The Wav2Vec2 tokenizer is only trained on uppercase characters so you'll need to make sure the text matches the tokenizer's vocabulary:
```py
>>> def uppercase(example):
... return {"transcription": e... | 98_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | >>> minds = minds.map(uppercase)
```
Now create a preprocessing function that:
1. Calls the `audio` column to load and resample the audio file.
2. Extracts the `input_values` from the audio file and tokenize the `transcription` column with the processor.
```py
>>> def prepare_dataset(batch):
... audio = batch... | 98_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ... batch["input_length"] = len(batch["input_values"][0])
... return batch
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up `map` by increasing the number of processes with the `num_proc` parameter. Remove the columns you don... | 98_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ```
🤗 Transformers doesn't have a data collator for ASR, so you'll need to adapt the [`DataCollatorWithPadding`] to create a batch of examples. It'll also dynamically pad your text and labels to the length of the longest element in its batch (instead of the entire dataset) so they are a uniform length. While it is p... | 98_3_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | Unlike other data collators, this specific data collator needs to apply a different padding method to `input_values` and `labels`:
```py
>>> import torch | 98_3_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | >>> from dataclasses import dataclass, field
>>> from typing import Any, Dict, List, Optional, Union
>>> @dataclass
... class DataCollatorCTCWithPadding:
... processor: AutoProcessor
... padding: Union[bool, str] = "longest" | 98_3_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
... # split inputs and labels since they have to be of different lengths and need
... # different padding methods
... input_features = [{"input_values": feature["input_values"][0]} f... | 98_3_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... labels = labels_batch["input_id... | 98_3_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#preprocess | .md | ... batch["labels"] = labels
... return batch
```
Now instantiate your `DataCollatorForCTCWithPadding`:
```py
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
``` | 98_3_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#evaluate | .md | Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [word error rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) metric (re... | 98_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#evaluate | .md | >>> wer = evaluate.load("wer")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the WER:
```py
>>> import numpy as np
>>> def compute_metrics(pred):
... pred_logits = pred.predictions
... pred_ids = np.argmax(pred_logits, axis=-1)
.... | 98_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#evaluate | .md | ... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
... pred_str = processor.batch_decode(pred_ids)
... label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
... wer = wer.compute(predictions=pred_str, references=label_str)
... return {"wer": wer}
```
... | 98_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | <frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You are now ready to start training your model! Load Wav2Vec2 with [`AutoModelForCTC`]. Specify the reduction to apply with th... | 98_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | >>> model = AutoModelForCTC.from_pretrained(
... "facebook/wav2vec2-base",
... ctc_loss_reduction="mean",
... pad_token_id=processor.tokenizer.pad_token_id,
... )
```
At this point, only three steps remain: | 98_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | ... pad_token_id=processor.tokenizer.pad_token_id,
... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=T... | 98_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to fine-tune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_asr_mind_model",
... per_device_train_batch_size=8,... | 98_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | ... warmup_steps=500,
... max_steps=2000,
... gradient_checkpointing=True,
... fp16=True,
... group_by_length=True,
... eval_strategy="steps",
... per_device_eval_batch_size=8,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... load_best_model_at_end=True,
..... | 98_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | >>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=encoded_minds["train"],
... eval_dataset=encoded_minds["test"],
... processing_class=processor,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... ) | 98_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | >>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so it can be accessible to everyone:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip> | 98_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#train | .md | ```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog [post](https://huggingface.co/blog/fine-tune-wav2vec2-english) for English ASR and this [post](https://huggingface.co/blog/fine-tune-... | 98_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | Great, now that you've fine-tuned a model, you can use it for inference!
Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!
```py
>>> from datasets import load_dataset, Audio | 98_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | >>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> audio_file = dataset[0]["audio"]["path"]
```
The simplest way to try out your fine-tuned model for inference is to... | 98_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | >>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
>>> transcriber(audio_file)
{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
```
<Tip>
The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better... | 98_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | </Tip>
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Load a processor to preprocess the audio file and transcription and return the `input` as PyTorch tensors:
```py
>>> from transformers import AutoProcessor | 98_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | >>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
```
Pass your inputs to the model and return the logits:
```py
>>> from transformers import AutoModelForCTC | 98_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | >>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the predicted `input_ids` with the highest probability, and use the processor to decode the predicted `input_ids` back into text:
```py
>>> import torch | 98_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/asr.md | https://huggingface.co/docs/transformers/en/tasks/asr/#inference | .md | >>> predicted_ids = torch.argmax(logits, dim=-1)
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription
['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
```
</pt>
</frameworkcontent> | 98_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/ | .md | <!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 99_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 99_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#causal-language-modeling | .md | [[open-in-colab]]
There are two types of language modeling, causal and masked. This guide illustrates causal language modeling.
Causal language models are frequently used for text generation. You can use these models for creative applications like
choosing your own text adventure or an intelligent coding assistant li... | 99_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#causal-language-modeling | .md | Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on
the left. This means the model cannot see future tokens. GPT-2 is an example of a causal language model.
This guide will show you how to:
1. Finetune [DistilGPT2](https://huggingface.co/distilbert/di... | 99_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#causal-language-modeling | .md | 2. Use your finetuned model for inference.
<Tip>
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/text-generation)
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transf... | 99_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#causal-language-modeling | .md | ```py
>>> from huggingface_hub import notebook_login | 99_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#causal-language-modeling | .md | >>> notebook_login()
``` | 99_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | Start by loading the first 5000 examples from the [ELI5-Category](https://huggingface.co/datasets/eli5_category) dataset with the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_data... | 99_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | >>> eli5 = load_dataset("eli5_category", split="train[:5000]")
```
Split the dataset's `train` split into a train and test set with the [`~datasets.Dataset.train_test_split`] method:
```py
>>> eli5 = eli5.train_test_split(test_size=0.2)
```
Then take a look at an example:
```py
>>> eli5["train"][0]
{'q_id': '7h... | 99_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | 'selftext': '',
'category': 'Economics',
'subreddit': 'explainlikeimfive',
'answers': {'a_id': ['dqnds8l', 'dqnd1jl', 'dqng3i1', 'dqnku5x'], | 99_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | 'text': ["The tax bill is 500 pages long and there were a lot of changes still going on right to the end. It's not just an adjustment to the income tax brackets, it's a whole bunch of changes. As such there is no good answer to your question. The big take aways are: - Big reduction in corporate income tax rate will mak... | 99_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | certain styles of business (law firms, hedge funds) extremely happy - Income tax changes are moderate, and are set to expire (though it's the kind of thing that might just always get re-applied without being made permanent) - People in high tax states (California, New York) lose out, and many of them will end up with t... | 99_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | 'None yet. It has to be reconciled with a vastly different house bill and then passed again.',
'Also: does this apply to 2017 taxes? Or does it start with 2018 taxes?',
'This article explains both the House and senate bills, including the proposed changes to your income taxes based on your income level. URL_0'],
'score... | 99_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#load-eli5-dataset | .md | 'title_urls': ['url'],
'selftext_urls': ['url']}
```
While this may look like a lot, you're only really interested in the `text` field. What's cool about language modeling
tasks is you don't need labels (also known as an unsupervised task) because the next word *is* the label. | 99_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | <Youtube id="ma1TrR7gE7I"/>
The next step is to load a DistilGPT2 tokenizer to process the `text` subfield:
```py
>>> from transformers import AutoTokenizer | 99_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | >>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilgpt2")
```
You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to
extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process#flatt... | 99_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | ```py
>>> eli5 = eli5.flatten()
>>> eli5["train"][0]
{'q_id': '7h191n',
'title': 'What does the tax bill that was passed today mean? How will it affect Americans in each tax bracket?',
'selftext': '',
'category': 'Economics',
'subreddit': 'explainlikeimfive',
'answers.a_id': ['dqnds8l', 'dqnd1jl', 'dqng3i1', 'dqnku5x']... | 99_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | 'answers.text': ["The tax bill is 500 pages long and there were a lot of changes still going on right to the end. It's not just an adjustment to the income tax brackets, it's a whole bunch of changes. As such there is no good answer to your question. The big take aways are: - Big reduction in corporate income tax rate ... | 99_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | will make certain styles of business (law firms, hedge funds) extremely happy - Income tax changes are moderate, and are set to expire (though it's the kind of thing that might just always get re-applied without being made permanent) - People in high tax states (California, New York) lose out, and many of them will end... | 99_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | 'None yet. It has to be reconciled with a vastly different house bill and then passed again.',
'Also: does this apply to 2017 taxes? Or does it start with 2018 taxes?',
'This article explains both the House and senate bills, including the proposed changes to your income taxes based on your income level. URL_0'],
'answe... | 99_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | 'title_urls': ['url'],
'selftext_urls': ['url']}
```
Each subfield is now a separate column as indicated by the `answers` prefix, and the `text` field is a list now. Instead
of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.
Here is a first preprocessing function ... | 99_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | ```py
>>> def preprocess_function(examples):
... return tokenizer([" ".join(x) for x in examples["answers.text"]])
```
To apply this preprocessing function over the entire dataset, use the 🤗 Datasets [`~datasets.Dataset.map`] method. You can speed up the `map` function by setting `batched=True` to process multip... | 99_3_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | ```py
>>> tokenized_eli5 = eli5.map(
... preprocess_function,
... batched=True,
... num_proc=4,
... remove_columns=eli5["train"].column_names,
... )
```
This dataset contains the token sequences, but some of these are longer than the maximum input length for the model.
You can now use a second prepr... | 99_3_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | You can now use a second preprocessing function to
- concatenate all the sequences
- split the concatenated sequences into shorter chunks defined by `block_size`, which should be both shorter than the maximum input length and short enough for your GPU RAM.
```py
>>> block_size = 128 | 99_3_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | >>> def group_texts(examples):
... # Concatenate all texts.
... concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
... total_length = len(concatenated_examples[list(examples.keys())[0]])
... # We drop the small remainder, we could add padding if the model supported it instead of ... | 99_3_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | ... if total_length >= block_size:
... total_length = (total_length // block_size) * block_size
... # Split by chunks of block_size.
... result = {
... k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
... for k, t in concatenated_examples.items()
... }
... ... | 99_3_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | ... return result
```
Apply the `group_texts` function over the entire dataset:
```py
>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
```
Now create a batch of examples using [`DataCollatorForLanguageModeling`]. It's more efficient to *dynamically pad* the
sentences to the longest l... | 99_3_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | <frameworkcontent>
<pt>
Use the end-of-sequence token as the padding token and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
```py
>>> from transformers import DataCollatorForLanguageModeling | 99_3_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#preprocess | .md | >>> tokenizer.pad_token = tokenizer.eos_token
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
```
</pt>
<tf>
Use the end-of-sequence token as the padding token and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
```py
>>> from transformers... | 99_3_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | <frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the [basic tutorial](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load DistilGPT2 with [`AutoModelForCausalLM`]:
```py
>>> from transformers import ... | 99_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `... | 99_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | 2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_eli5_clm-model",
... eval_strategy="epoch",
... learning_rate=2e-5,
... weight_dec... | 99_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=lm_dataset["train"],
... eval_dataset=lm_dataset["test"],
... data_collator=data_collator,
... tokenizer=tokenizer,
... )
>>> trainer.train()
```
Once training is completed, use the [`~transformers.Trainer.evalu... | 99_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> eval_results = trainer.evaluate()
>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
Perplexity: 49.61
```
Then share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't ... | 99_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | </Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer, AdamWeightDecay | 99_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Then you can load DistilGPT2 with [`TFAutoModelForCausalLM`]:
```py
>>> from transformers import TFAutoModelForCausalLM | 99_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> model = TFAutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... lm_dataset["train"],
... shuffle=True,
... batch_size=... | 99_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> tf_test_set = model.prepare_tf_dataset(
... lm_dataset["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a... | 99_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> model.compile(optimizer=optimizer) # No loss argument!
```
This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback | 99_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | >>> callback = PushToHubCallback(
... output_dir="my_awesome_eli5_clm-model",
... tokenizer=tokenizer,
... )
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and y... | 99_4_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | ```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to finetune a model for causal language modeli... | 99_4_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/language_modeling.md | https://huggingface.co/docs/transformers/en/tasks/language_modeling/#train | .md | [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
</Tip> | 99_4_12 |
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