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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbart50tokenizerfast | .md | Args:
vocab_file (`str`):
Path to the vocabulary file.
src_lang (`str`, *optional*):
A string representing the source language.
tgt_lang (`str`, *optional*):
A string representing the target language.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"</s>"`):
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for | 146_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbart50tokenizerfast | .md | The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.
cls_token (`str`, *optional*, defaults to `"<s>"`):
The classifier token which is used when doing sequence classification (classification of the whole sequence | 146_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbart50tokenizerfast | .md | The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`): | 146_11_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbart50tokenizerfast | .md | token instead.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
mask_token (`str`, *optional*, defaults to `"<mask>"`):
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.
Examples:
```python
>>> from transformers import MBart50TokenizerFast | 146_11_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbart50tokenizerfast | .md | >>> tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
>>> # model(**model_inputs) should work
```
<frameworkcontent>
<pt> | 146_11_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartmodel | .md | The bare MBART Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 146_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartmodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MBartConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 146_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartmodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | 146_12_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforconditionalgeneration | .md | The MBART Model with a language modeling head. Can be used for summarization, after fine-tuning the pretrained models.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 146_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforconditionalgeneration | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MBartConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 146_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforconditionalgeneration | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | 146_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforquestionanswering | .md | MBART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) | 146_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforquestionanswering | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MBartConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not | 146_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforquestionanswering | .md | Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | 146_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforsequenceclassification | .md | MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 146_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforsequenceclassification | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MBartConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 146_15_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforsequenceclassification | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights. | 146_15_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#mbartforcausallm | .md | No docstring available for MBartForCausalLM
Methods: forward
</pt>
<tf> | 146_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#tfmbartmodel | .md | No docstring available for TFMBartModel
Methods: call | 146_17_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#tfmbartforconditionalgeneration | .md | No docstring available for TFMBartForConditionalGeneration
Methods: call
</tf>
<jax> | 146_18_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#flaxmbartmodel | .md | No docstring available for FlaxMBartModel
Methods: __call__
- encode
- decode | 146_19_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#flaxmbartforconditionalgeneration | .md | No docstring available for FlaxMBartForConditionalGeneration
Methods: __call__
- encode
- decode | 146_20_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#flaxmbartforsequenceclassification | .md | No docstring available for FlaxMBartForSequenceClassification
Methods: __call__
- encode
- decode | 146_21_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mbart.md | https://huggingface.co/docs/transformers/en/model_doc/mbart/#flaxmbartforquestionanswering | .md | No docstring available for FlaxMBartForQuestionAnswering
Methods: __call__
- encode
- decode
</jax>
</frameworkcontent> | 146_22_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/ | .md | <!--Copyright 2022 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 agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | 147_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/ | .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 properly in your Markdown viewer.
--> | 147_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#overview | .md | The Whisper model was proposed in [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
The abstract from the paper is the following: | 147_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#overview | .md | *We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and | 147_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#overview | .md | for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.* | 147_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#overview | .md | This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ). The Tensorflow version of this model was contributed by [amyeroberts](https://huggingface.co/amyeroberts).
The original code can be found [here](https://github.com/openai/whisper). | 147_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#quick-usage | .md | You can run Whisper in less than 4 lines of code and transcribe in less than a minute!
```python
# pip install transformers torch
import torch
from transformers import pipeline
whisper = pipeline("automatic-speech-recognition", "openai/whisper-large-v3", torch_dtype=torch.float16, device="cuda:0")
transcription = whisper("<audio_file.mp3>") | 147_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#quick-usage | .md | transcription = whisper("<audio_file.mp3>")
print(transcription["text"])
```
Voila! You can swap the model with any [Whisper checkpoints](https://huggingface.co/models?other=whisper&sort=downloads) on the Hugging Face Hub with the same pipeline based on your needs.
Bonus: You can replace `"cuda"` with `"mps"` to make it seamlessly work on Macs. | 147_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#usage-tips | .md | - The model usually performs well without requiring any finetuning.
- The architecture follows a classic encoder-decoder architecture, which means that it relies on the [`~generation.GenerationMixin.generate`] function for inference.
- One can use [`WhisperProcessor`] to prepare audio for the model, and decode the predicted ID's back into text.
- To convert the model and the processor, we recommend using the following:
```bash | 147_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#usage-tips | .md | - To convert the model and the processor, we recommend using the following:
```bash
python src/transformers/models/whisper/convert_openai_to_hf.py --checkpoint_path "" --pytorch_dump_folder_path "Arthur/whisper-3" --convert_preprocessor True
```
The script will automatically determine all necessary parameters from the OpenAI checkpoint. A `tiktoken` library needs to be installed
to perform the conversion of the OpenAI tokenizer to the `tokenizers` version. | 147_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | Here is a step-by-step guide to transcribing an audio sample using a pre-trained Whisper model:
```python
>>> from datasets import load_dataset
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> # Select an audio file and read it:
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> audio_sample = ds[0]["audio"] | 147_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Load the Whisper model in Hugging Face format:
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> # Use the model and processor to transcribe the audio:
>>> input_features = processor(
... audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt"
... ).input_features
>>> # Generate token ids
>>> predicted_ids = model.generate(input_features) | 147_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # Decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | 147_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> transcription[0]
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```
Whisper is compatible with the following optimisations for both short and long-form generation:
- [PyTorch Scaled Dot Product Attention (SDPA)](../perf_infer_gpu_one#pytorch-scaled-dot-product-attention): flash attention and memory-efficient attention kernels. Enabled by default for `torch>=2.1.1`. | 147_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | - [Flash Attention 2](../perf_infer_gpu_one#flashattention-2): improved implementation of flash attention through better parallelism and work partitioning.
- [torch.compile](../llm_optims#static-kv-cache-and-torchcompile): JIT-compile the forward pass to dispatch to efficient fused kernels.
As an example, the following codesnippet enables SDPA and `torch.compile` for up to 5x faster inference:
```python
>>> from datasets import load_dataset | 147_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | ```python
>>> from datasets import load_dataset
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration | 147_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Select an audio file and read it:
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> audio_sample = ds[0]["audio"]
>>> # Load the Whisper model with SDPA attention
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", attn_implementation="sdpa") | 147_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Enable static cache and compile the forward pass
>>> model.generation_config.cache_implementation = "static"
>>> model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
>>> # Use the model and processor to transcribe the audio:
>>> input_features = processor(
... audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt"
... ).input_features
>>> # Compile the forward pass
>>> for _ in range(2):
>>> model.generate(input_features) | 147_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Compile the forward pass
>>> for _ in range(2):
>>> model.generate(input_features)
>>> # Generate token ids using compiled graph (fast!)
>>> predicted_ids = model.generate(input_features)
>>> # Decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | 147_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#inference | .md | >>> # Decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
>>> transcription[0]
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```
For more details on each optimisation, refer to the documentation linked above. | 147_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Whisper. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
- [Fine-tune Whisper](https://huggingface.co/blog/fine-tune-whisper) on your own dataset for better downstream performance. | 147_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#resources | .md | - [Fine-tune Whisper](https://huggingface.co/blog/fine-tune-whisper) on your own dataset for better downstream performance.
- [Distil-Whisper](https://huggingface.co/distil-whisper): Upto 6x faster, 2x smaller distilled Whisper models for English. We release the [model checkpoints](https://huggingface.co/distil-whisper), and [distillation code](https://github.com/huggingface/distil-whisper). | 147_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#resources | .md | - A fork with a script to [convert a Whisper model in Hugging Face format to OpenAI format](https://github.com/zuazo-forks/transformers/blob/convert_hf_to_openai/src/transformers/models/whisper/convert_hf_to_openai.py). 🌎
Usage example:
```bash
pip install -U openai-whisper
python convert_hf_to_openai.py \
--checkpoint openai/whisper-tiny \
--whisper_dump_path whisper-tiny-openai.pt
``` | 147_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | This is the configuration class to store the configuration of a [`WhisperModel`]. It is used to instantiate a
Whisper model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the Whisper
[openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | 147_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 51865):
Vocabulary size of the Whisper model. Defines the number of different tokens that can be represented by the
`decoder_input_ids` passed when calling [`WhisperModel`]
num_mel_bins (`int`, *optional*, defaults to 80): | 147_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | `decoder_input_ids` passed when calling [`WhisperModel`]
num_mel_bins (`int`, *optional*, defaults to 80):
Number of mel features used per input features. Should correspond to the value used in the
`WhisperProcessor` class.
encoder_layers (`int`, *optional*, defaults to 4):
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 4):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 6): | 147_6_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the Transformer decoder.
encoder_ffn_dim (`int`, *optional*, defaults to 1536):
Dimensionality of the "intermediate" (often named feed-forward) layer in encoder.
decoder_ffn_dim (`int`, *optional*, defaults to 1536): | 147_6_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | decoder_ffn_dim (`int`, *optional*, defaults to 1536):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details. | 147_6_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
decoder_start_token_id (`int`, *optional*, defaults to 50257):
Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
are provided to the `generate` function. It is used to guide the model`s generation process depending on
the task.
use_cache (`bool`, *optional*, defaults to `True`): | 147_6_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | the task.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
Whether the model is used as an encoder/decoder or not.
activation_function (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported. | 147_6_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | `"relu"`, `"silu"` and `"gelu_new"` are supported.
d_model (`int`, *optional*, defaults to 384):
Dimensionality of the layers.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer. | 147_6_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_embedding (`bool`, *optional*, defaults to False):
Scale embeddings by diving by sqrt(d_model).
max_source_positions (`int`, *optional*, defaults to 1500): | 147_6_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | Scale embeddings by diving by sqrt(d_model).
max_source_positions (`int`, *optional*, defaults to 1500):
The maximum sequence length of log-mel filter-bank features that this model might ever be used with.
max_target_positions (`int`, *optional*, defaults to 448):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
pad_token_id (`int`, *optional*, defaults to 50256):
Padding token id. | 147_6_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | just in case (e.g., 512 or 1024 or 2048).
pad_token_id (`int`, *optional*, defaults to 50256):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 50256):
Begin of stream token id.
eos_token_id (`int`, *optional*, defaults to 50256):
End of stream token id.
suppress_tokens (`List[int]`, *optional*):
A list containing the non-speech tokens that will be used by the logit processor in the `generate` | 147_6_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | A list containing the non-speech tokens that will be used by the logit processor in the `generate`
function. NON_SPEECH_TOKENS and NON_SPEECH_TOKENS_MULTI each correspond to the `english-only` and the
`multilingual` model.
begin_suppress_tokens (`List[int]`, *optional*, defaults to `[220,50256]`):
A list containing tokens that will be supressed at the beginning of the sampling process. Initialized as
the token for `" "` (`blank_token_id`) and the `eos_token_id` | 147_6_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | the token for `" "` (`blank_token_id`) and the `eos_token_id`
use_weighted_layer_sum (`bool`, *optional*, defaults to `False`):
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
instance of [`WhisperForAudioClassification`].
classifier_proj_size (`int`, *optional*, defaults to 256):
Dimensionality of the projection before token mean-pooling for classification. Only relevant when using an
instance of [`WhisperForAudioClassification`]. | 147_6_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | instance of [`WhisperForAudioClassification`].
apply_spec_augment (`bool`, *optional*, defaults to `False`):
Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see
[SpecAugment: A Simple Data Augmentation Method for Automatic Speech
Recognition](https://arxiv.org/abs/1904.08779).
mask_time_prob (`float`, *optional*, defaults to 0.05):
Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking | 147_6_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking
procecure generates `mask_time_prob*len(time_axis)/mask_time_length` independent masks over the axis. If
reasoning from the propability of each feature vector to be chosen as the start of the vector span to be
masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the | 147_6_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the
actual percentage of masked vectors. This is only relevant if `apply_spec_augment == True`.
mask_time_length (`int`, *optional*, defaults to 10):
Length of vector span along the time axis.
mask_time_min_masks (`int`, *optional*, defaults to 2),:
The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, | 147_6_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step,
irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length <
mask_time_min_masks''
mask_feature_prob (`float`, *optional*, defaults to 0.0):
Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The
masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over | 147_6_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | masking procecure generates `mask_feature_prob*len(feature_axis)/mask_time_length` independent masks over
the axis. If reasoning from the propability of each feature vector to be chosen as the start of the vector
span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap
may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is
True`.
mask_feature_length (`int`, *optional*, defaults to 10): | 147_6_17 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | True`.
mask_feature_length (`int`, *optional*, defaults to 10):
Length of vector span along the feature axis.
mask_feature_min_masks (`int`, *optional*, defaults to 0),:
The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time
step, irrespectively of `mask_feature_prob`. Only relevant if
`mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks`.
median_filter_width (`int`, *optional*, defaults to 7): | 147_6_18 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | median_filter_width (`int`, *optional*, defaults to 7):
Width of the median filter used to smoothen to cross-attention outputs when computing token timestamps.
Should be an odd number.
Example:
```python
>>> from transformers import WhisperConfig, WhisperModel | 147_6_19 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperconfig | .md | >>> # Initializing a Whisper tiny style configuration
>>> configuration = WhisperConfig()
>>> # Initializing a model (with random weights) from the tiny style configuration
>>> model = WhisperModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 147_6_20 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | Construct a Whisper tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
normalizer_file (`str`, *optional*):
Path to the normalizer_file file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See | 147_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): | 147_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as
`"<|startoftranscript|>"` when generating.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
pad_token (`str`, *optional*):
The token used for padding, for example when batching sequences of different lengths.
add_prefix_space (`bool`, *optional*, defaults to `False`): | 147_7_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word.
language (`str`, *optional*):
The language of the transcription text. The corresponding language id token is appended to the start of the
sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token | 147_7_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token
`"<|es|>"` is appended to the start of sequence. This should be used for multilingual fine-tuning only.
task (`str`, *optional*):
Task identifier to append at the start of sequence (if any). This should be used for mulitlingual
fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation.
predict_timestamps (`bool`, *optional*, defaults to `False`): | 147_7_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizer | .md | predict_timestamps (`bool`, *optional*, defaults to `False`):
Whether to omit the `<|notimestamps|>` token at the start of the sequence.
Methods: set_prefix_tokens
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
- batch_decode
- decode
- basic_normalize
- normalize | 147_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | Construct a "fast" Whisper tokenizer (backed by HuggingFace's *tokenizers* library).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
normalizer_file (`str`, *optional*):
Path to the normalizer_file file.
tokenizer_file (`str`, *optional*): | 147_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | normalizer_file (`str`, *optional*):
Path to the normalizer_file file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything needed to load the tokenizer.
unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`): | 147_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | token instead.
bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The beginning of sequence token. The `decoder_start_token_id` is used to set the first token as
`"<|startoftranscript|>"` when generating.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
add_prefix_space (`bool`, *optional*, defaults to `False`):
Whether or not to add an initial space to the input. This allows to treat the leading word just as any | 147_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (Whisper tokenizer detect beginning of words by the preceding space).
language (`str`, *optional*):
The language of the transcription text. The corresponding language id token is appended to the start of the
sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token | 147_8_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | sequence for multilingual speech recognition and speech translation tasks, e.g. for Spanish the token
`"<|es|>"` is appended to the start of sequence. This should be used for multilingual fine-tuning only.
task (`str`, *optional*):
Task identifier to append at the start of sequence (if any). This should be used for mulitlingual
fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation.
predict_timestamps (`bool`, *optional*, defaults to `False`): | 147_8_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispertokenizerfast | .md | predict_timestamps (`bool`, *optional*, defaults to `False`):
Whether to omit the `<|notimestamps|>` token at the start of the sequence.
Methods: set_prefix_tokens
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
- batch_decode
- decode
- basic_normalize
- normalize | 147_8_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperfeatureextractor | .md | Constructs a Whisper feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the `Short Time
Fourier Transform` which should match pytorch's `torch.stft` equivalent.
Args: | 147_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperfeatureextractor | .md | Fourier Transform` which should match pytorch's `torch.stft` equivalent.
Args:
feature_size (`int`, *optional*, defaults to 80):
The feature dimension of the extracted features.
sampling_rate (`int`, *optional*, defaults to 16000):
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
hop_length (`int`, *optional*, defaults to 160):
Length of the overlaping windows for the STFT used to obtain the Mel Frequency coefficients. | 147_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperfeatureextractor | .md | Length of the overlaping windows for the STFT used to obtain the Mel Frequency coefficients.
chunk_length (`int`, *optional*, defaults to 30):
The maximum number of chuncks of `sampling_rate` samples used to trim and pad longer or shorter audio
sequences.
n_fft (`int`, *optional*, defaults to 400):
Size of the Fourier transform.
padding_value (`float`, *optional*, defaults to 0.0):
Padding value used to pad the audio. Should correspond to silences.
Methods: __call__ | 147_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperprocessor | .md | Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
processor.
[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
the [`~WhisperProcessor.__call__`] and [`~WhisperProcessor.decode`] for more information.
Args:
feature_extractor (`WhisperFeatureExtractor`):
An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`WhisperTokenizer`): | 147_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperprocessor | .md | An instance of [`WhisperFeatureExtractor`]. The feature extractor is a required input.
tokenizer (`WhisperTokenizer`):
An instance of [`WhisperTokenizer`]. The tokenizer is a required input.
Methods: __call__
- from_pretrained
- save_pretrained
- batch_decode
- decode
<frameworkcontent>
<pt> | 147_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispermodel | .md | The bare Whisper Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 147_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispermodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`WhisperConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 147_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whispermodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- _mask_input_features | 147_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforconditionalgeneration | .md | The Whisper Model with a language modeling head. Can be used for automatic speech recognition.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 147_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforconditionalgeneration | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`WhisperConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 147_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforconditionalgeneration | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward
- generate | 147_12_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforcausallm | .md | Whisper decoder with a language modeling head on top (linear layer with weights tied to the input embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. | 147_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforcausallm | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`WhisperConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the | 147_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforcausallm | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 147_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforaudioclassification | .md | Whisper Encoder Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
like SUPERB Keyword Spotting.
Args:
input_features (`torch.FloatTensor` of shape `(batch_size, feature_size, sequence_length)`):
Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via | 147_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforaudioclassification | .md | loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
[`AutoFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): | 147_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforaudioclassification | .md | head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) | 147_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforaudioclassification | .md | Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*): | 147_14_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#whisperforaudioclassification | .md | tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
Methods: forward
</pt>
<tf> | 147_14_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/whisper.md | https://huggingface.co/docs/transformers/en/model_doc/whisper/#tfwhispermodel | .md | No docstring available for TFWhisperModel
Methods: call | 147_15_0 |
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