text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
For an overview of generation strategies and code examples, check out the [following
guide](../generation_strategies).
</Tip> | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Parameters:
inputs (`tf.Tensor` of varying shape depending on the modality, *optional*):
The sequence used as a prompt for the generation or as model inputs to the encoder. If unset the method
initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
default values, whose documentation should be checked to parameterize generation.
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Whether to return the timestamps with the text. This enables the `TFWhisperTimestampsLogitsProcessor`.
task (`str`, *optional*):
Task to use for generation, either "translate" or "transcribe". The `model.config.forced_decoder_ids`
will be updated accordingly.
lang... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
transcription, e.g. custom vocabularies or proper nouns to make it more likely to predict those words
correctly. It cannot be used in conjunction with `decoder_start_token_id` as it overwrites this value.
return_token_timestamps (`bool`, *optional*):
Whether to return token-l... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
Return:
[`~utils.ModelOutput`] or `tf.Tensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` or when
`config.return_dict_in_generate=True`) or a `tf.Tensor`.
If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
- [`~generation.TFGreedySearchEncoderDecoderOutput`],
- [`~generation.TFSampleEncoderDecoderOutput`],
- [`~generation.TFBeamSearchEncoderDecoderOutput`],
- [`~generation.TFBeamSampleEncoderDecoderOutput`]
"""
if generation_config is None:
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
generation_config.return_timestamps = return_timestamps
else:
generation_config.return_timestamps = False
if language is not None:
language = language.lower()
generation_config.language = language
if task is not None:
generation_config.task = task... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if task is not None or language is not None or (forced_decoder_ids is None and prompt_ids is not None):
forced_decoder_ids = []
if hasattr(generation_config, "language"):
if generation_config.language in generation_config.lang_to_id.keys():
language_token = ge... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
f" {list(TO_LANGUAGE_CODE.values()) if is_language_code else list(TO_LANGUAGE_CODE.keys())}."
)
if language_token not in generation_config.lang_to_id:
raise ValueError(
f"{language_token} is not supported by this specific model as it is not... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if hasattr(generation_config, "task"):
if generation_config.task in TASK_IDS:
forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
else:
raise ValueError(
f"The `{generation_config.task}`task ... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if prompt_ids is not None:
if kwargs.get("decoder_start_token_id") is not None:
raise ValueError(
"When specifying `prompt_ids`, you cannot also specify `decoder_start_token_id` as it gets overwritten."
)
prompt_ids = prompt_ids.tolist()
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# Update the max generation length to include the prompt
specified_max_length = kwargs.pop("max_new_tokens", None) or kwargs.pop("max_length", None)
default_max_length = generation_config.max_new_tokens or generation_config.max_length
non_prompt_max_length = specified_max_length or d... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
# Reformat the forced_decoder_ids to incorporate the prompt
non_prompt_forced_decoder_ids = (
kwargs.pop("forced_decoder_ids", None) or generation_config.forced_decoder_ids
)
forced_decoder_ids = [
*text_prompt_ids,
generation_config.de... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if return_token_timestamps:
kwargs["output_attentions"] = True
kwargs["return_dict_in_generate"] = True
if getattr(generation_config, "task", None) == "translate":
logger.warning("Token-level timestamps may not be reliable for task 'translate'.")
if not h... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if return_token_timestamps and hasattr(generation_config, "alignment_heads"):
outputs["token_timestamps"] = self._extract_token_timestamps(outputs, generation_config.alignment_heads)
return outputs
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.conf... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
cross_attentions=cross_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_st... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
if decoder_attention_mask is not None: # xla
decoder_position_ids = tf.math.cumsum(decoder_attention_mask, axis=-1, exclusive=True)[:, -1:]
elif past_key_values is not None: # no xla + past
decoder_position_ids = past_key_values[0][0].shape[2]
else: # no xla + no past
... | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "model", None) is not None:
with tf.name_scope(self.model.name):
self.model.build(None) | 9,916 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_tf_whisper.py |
class WhisperTokenizer(PreTrainedTokenizer):
"""
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. | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
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 fol... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
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`):
Whether or not to add an initial space to the input. This allows to treat th... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
fine-tuning, with `"transcribe"` for speech recognition and `"translate"` for speech translation.
predict_timestamps (`bool`, *optional*, defaults to `False`):
Whether to omit the `<|notimestamps|>` token at the start of the sequence.
""" | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"] | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def __init__(
self,
vocab_file,
merges_file,
normalizer_file=None,
errors="replace",
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
pad_token=None,
add_prefix_space=False,
language=None,
tas... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
AddedToken(pad_token, lstrip=False, rstrip=False, normalized=False, special=True)
if isinstance(pad_token, str)
else pad_token
) | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
with open(vocab_file, encoding="utf-8") as vocab_handle:
self.encoder = json.load(vocab_handle)
self.decoder = {v: k for k, v in self.encoder.items()}
self.errors = errors # how to handle errors in decoding
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
self.timestamp_pat = re.compile(r"<\|(\d+\.\d+)\|>")
self.language = language
supe... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe with GPT2 -> Whisper
def bpe(self, token):
if token in self.cache:
return self.cache[token]
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
```python
>>> # instantiate the tokenizer and set the prefix token to Spanish
>>> tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="spanish")
>>> # now switch the prefix token from Spanish to French
>>> tokenizer.set_prefix_tokens(language="french")
``... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
Args:
language (`str`, *optional*, defaults to `None`):
The language of the transcription text.
task (`str`, *optional*, defaults to `None`):
Task identifier to append at the start of sequence (if any).
predict_timestamps (`bool`, *optional*, defaults ... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
@property
def prefix_tokens(self) -> List[int]:
bos_token_id = self.convert_tokens_to_ids("<|startoftranscript|>")
translate_token_id = self.convert_tokens_to_ids("<|translate|>")
transcribe_token_id = self.convert_tokens_to_ids("<|transcribe|>")
notimestamps_token_id = self.convert_... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if self.language is not None:
self.language = self.language.lower()
if self.language in TO_LANGUAGE_CODE:
language_id = TO_LANGUAGE_CODE[self.language]
elif self.language in TO_LANGUAGE_CODE.values():
language_id = self.language
else:
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
bos_sequence = [bos_token_id]
if self.language is not None:
bos_sequence.append(bos_token_id + 1 + langs.index(language_id))
if self.task is not None:
bos_sequence.append(transcribe_token_id if self.task == "transcribe" else translate_token_id)
if not self.predict_timesta... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# Copied from transformers.models.speech_to_text.tokenization_speech_to_text.Speech2TextTokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retri... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
prefix_ones = [1] * len(self.prefix_tokens)
suffix_ones = [1]
if token_ids_1 is None:
re... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize with GPT2 -> Whisper
def _tokenize(self, text):
"""Tokenize a string."""
bpe_tokens = []
for token in re.findall(self.pat, text):
token = "".join(
self.byte_encoder[b] for b in token.... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def _convert_id_to_token(self, index):
"""
Converts an index (integer) in a token (str) using the vocab. Whisper's base tokenizer always decodes OOV
tokens as "", thus we do not use the `unk_token` here.
"""
return self.decoder.get(index, "")
def _normalize(self, text):
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def normalize(self, text):
"""
Normalize a given string using the `EnglishTextNormalizer` class, which preforms commons transformation on
english text.
"""
normalizer = EnglishTextNormalizer(self.english_spelling_normalizer)
return normalizer(text)
@staticmethod
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def _decode_with_timestamps(
self, token_ids, skip_special_tokens=False, time_precision=0.02, segment_size=1500
) -> str:
"""
Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. This method decodes
given tokens with timestamps tokens annotated, e.g.... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if timestamp < cur_max_timestamp:
# next segment has started
last_was_single_ending = i >= 2 and not (
token_ids[i - 1] >= timestamp_begin and token_ids[i - 2] >= timestamp_begin
)
if last_was_single_ending:
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
outputs.append(f"<|{(timestamp + prev_segments_len):.2f}|>")
outputs.append([])
else:
outputs[-1].append(token)
outputs = [
s if isinstance(s, str) else self.decode(s, skip_special_tokens=skip_special_tokens) for s in outputs
]
return "".jo... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
consecutive = np.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + 1
if consecutive.shape[0] == 0 and timestamp_tokens.sum() <= 1:
# either there are no timestamps or there are no consecutive ones
return []
elif np.where(timestamp_tokens)[0][-1] + 1 not in consecutive:
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if start_timestamp_position < cur_max_timestamp:
# next segment has started
is_single_ending = last_slice >= 2 and not (
token_ids[last_slice - 2] >= timestamp_begin and token_ids[last_slice - 1] >= timestamp_begin
)
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# strip timestamp tokens from the text output
sliced_tokens = self._preprocess_token_ids(sliced_tokens)
text = self._decode(sliced_tokens)
text = self._filter_timestamp_ids(text)
offsets.append(
{
"text": text,
... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
Args:
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
"""
return self.convert_tokens_to_ids([("<|%.2f|>" % (i * time_precision)) for i in range(1500 + 1)])
def _preprocess_token_ids(self, token_ids, skip_special_token... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Typically, obtained using the `__call__` method of the tokenizer.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove s... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def decode(
self,
token_ids,
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
output_offsets: bool = False,
time_precision: float = 0.02,
decode_with_timestamps: bool = False,
normalize: bool = False,
basic_normalize: b... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
Args:
token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the ... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
time_precision (`float`, *optional*, defaults to 0.02):
The time ratio to convert from token to time.
decode_with_timestamps (`bool`, *optional*, defaults to `False`):
Whether or not to decode with timestamps included in the raw text.
normalize (`bool`, *optional*... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
destroy information in the decoded text, hence it should be used with caution.
kwargs (additional keyword arguments, *optional*):
Will be passed to the underlying model specific decode method.
Returns:
`str`: The decoded sentence.
"""
filtered_ids = self._... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
text = super().decode(
filtered_ids,
skip_special_tokens=skip_special_tokens,
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
normalize=normalize,
basic_normalize=basic_normalize,
remove_diacritics=remove_diacritics,
**kwargs... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
normalize: bool = False,
basic_normalize: bool = False,
remove_diacritics: bool = False,
**kwargs,
) -> str:
self._decode_use_source_tokenizer = kwargs.pop("use_sou... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
sub_texts = []
current_sub_text = []
for token in filtered_tokens:
if ... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
if normalize:
clean_text = self.normalize(text)
return clean_text
elif basic_normalize:
clean_text = self.basic_normalize(text, remove_diacritics=remove_diacritics)
return clean_text
else:
return text
# Copied from transformers.models.gpt2... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (file... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.prepare_for_tokenization with GPT2 -> Whisper
def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
if is_split_into_words or add_p... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
self.set_prefix_tokens(task=task, language=language, predict_timestamps=not no_timestamps)
# prefix tokens are of the form: <|startoftranscript|> <|lang_id|> <|task|> <|notimestamps|>
# we don't want to force the bos... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
def get_prompt_ids(self, text: str, return_tensors="np"):
"""Converts prompt text to IDs that can be passed to [`~WhisperForConditionalGeneration.generate`]."""
batch_encoding = self("<|startofprev|>", " " + text.strip(), add_special_tokens=False)
# Check for special tokens
prompt_text_... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
# handle case of empty token_ids for decoding with timestamps.
# at this point token_ids is a list, so it is safe to use if not check.
if not token_ids:
return token_ids
has_prompt = token_ids[0] == prompt_token_id
if has_prompt:
if decoder_start_token_id in toke... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
@staticmethod
def _convert_to_list(token_ids):
# convert type to ndarray if necessary
if hasattr(token_ids, "numpy"):
if "torch" in str(type(token_ids)):
token_ids = token_ids.cpu().numpy()
elif "tensorflow" in str(type(token_ids)):
token_ids =... | 9,917 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper.py |
class WhisperConfig(PretrainedConfig):
r"""
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 simi... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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):
... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
encode... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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`):
Whether or not the model should return the last key/values attentions (not used by all models).
is_encoder_decode... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initi... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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 logi... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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`].
apply_spec_augment (... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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
actual percentage of masked vectors. This is only relevant if `apply_spec_augment =... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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_le... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
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. | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
Example:
```python
>>> from transformers import WhisperConfig, WhisperModel
>>> # Initializing a Whisper tiny style configuration
>>> configuration = WhisperConfig()
>>> # Initializing a model (with random weights) from the tiny style configuration
>>> model = WhisperModel(configuration)
... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
def __init__(
self,
vocab_size=51865,
num_mel_bins=80,
encoder_layers=4,
encoder_attention_heads=6,
decoder_layers=4,
decoder_attention_heads=6,
decoder_ffn_dim=1536,
encoder_ffn_dim=1536,
encoder_layerdrop=0.0,
decoder_layerdrop=0.... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
mask_time_min_masks=2,
mask_feature_prob=0.0,
mask_feature_length=10,
mask_feature_min_masks=0,
median_filter_width=7,
**kwargs,
):
self.vocab_size = vocab_size
self.num_mel_bins = num_mel_bins
self.d_model = d_model
self.encoder_layers = encod... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
# Audio Classification-specific parameters. Feel free to ignore for other classes.
self.classifier_proj_size = classifier_proj_size
self.use_weighted_layer_sum = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
self.apply_spec_augm... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=decoder_start_token_id,
suppress_tokens=suppress_tokens,
begin_suppress_tok... | 9,918 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
class WhisperOnnxConfig(OnnxSeq2SeqConfigWithPast):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
common_inputs = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
]
)
if self.use_past:
... | 9,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
def generate_dummy_inputs(
self,
preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
sampling_rate: int = 22050,
time_duratio... | 9,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
decoder_inputs = super().generate_dummy_inputs(
preprocessor.tokenizer, batch_size, seq_length, is_pair, framework
)
dummy_inputs["input_features"] = encoder_inputs.pop("input_features")
dummy_inputs["decoder_input_ids"] = decoder_inputs.pop("decoder_input_ids")
if "past_ke... | 9,919 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/configuration_whisper.py |
class WhisperGenerationMixin(GenerationMixin):
def _extract_token_timestamps(
self, generate_outputs, alignment_heads, time_precision=0.02, num_frames=None, num_input_ids=None
):
"""
Calculates token-level timestamps using the encoder-decoder cross-attentions and dynamic time-warping (DT... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Select specific cross-attention layers and heads. This is a tensor
# of shape (batch size, num selected, output length, input length).
weights = torch.stack([cross_attentions[l][:, h] for l, h in alignment_heads])
weights = weights.permute([1, 0, 2, 3])
weight_length = None
i... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# beam search takes `decoder_input_ids` into account in the `beam_indices` length
# but forgot to shift the beam_indices by the number of `decoder_input_ids`
beam_indices = torch.zeros_like(generate_outputs.beam_indices[:, :weight_length])
# we actually shif the beam indices here
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Select the cross attention from the right beam for each output sequences
weights = torch.stack(
[
torch.index_select(weights[:, :, i, :], dim=0, index=beam_indices[:, i])
for i in range(beam_indices.shape[1])
],
dim=2,... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# we're using np.unique because num_frames can be int/list/tuple
if isinstance(num_frames, int):
weights = weights[..., : num_frames // 2]
elif isinstance(num_frames, (list, tuple, np.ndarray)) and len(np.unique(num_frames)) == 1:
weights = weights[..., : num_fra... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if num_frames is None or isinstance(num_frames, int):
# Normalize and smoothen the weights.
std = torch.std(weights, dim=-2, keepdim=True, unbiased=False)
mean = torch.mean(weights, dim=-2, keepdim=True)
weights = (weights - mean) / std
weights = _median_filte... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Normalize and smoothen the weights.
std = torch.std(matrix, dim=-2, keepdim=True, unbiased=False)
mean = torch.mean(matrix, dim=-2, keepdim=True)
matrix = (matrix - mean) / std
matrix = _median_filter(matrix, self.config.median_filter_width)
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def generate(
self,
input_features: Optional[torch.Tensor] = None,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Ca... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
no_speech_threshold: Optional[float] = None,
num_segment_frames: Optional[int] = None,
attention_mask: Optional[torch.Tensor] = None,
time_precision: float = 0.02,
time_precision_features: float = 0.01,
return_token_timestamps: Optional[bool] = None,
return_segments: bool... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
<Tip warning={true}>
Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
model's default generation configuration. You can override any `generation_config` by passing the corresponding
parameters to generate(), e.g. `.generate(inputs, n... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
Parameters:
input_features (`torch.Tensor` of shape `(batch_size, feature_size, sequence_length)`, *optional*):
Float values of log-mel features extracted from the raw speech waveform. The raw speech waveform can be obtained by
loading a `.flac` or `.wav` audio file into an a... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
passed to generate matching the attributes of `generation_config` will override them. If
`generation_config` is not provided, the default will be used, which had the following loading
priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
Custom stopping criteria that complement the default stopping criteria built from arguments and a
generation config. If a stopping criteria is passed that is already created with the arguments or a
generation config an error is thrown. This feature is intended for advanced users.
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
Retrieval](https://arxiv.org/abs/2010.00904).
synced_gpus (`bool`, *optional*, defaults to `False`):
Whether to continue running the while loop until max_length (needed to avoid deadlocking with
`FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
return_timest... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
tokens in the `model.generation_config.lang_to_id` dictionary.
is_multilingual (`bool`, *optional*):
Whether or not the model is multilingual.
prompt_ids (`torch.Tensor`, *optional*):
Rank-1 tensor of token IDs created by passing text to [`~WhisperProcessor.get_pr... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
Only relevant for long-form transcription. Condition type of `prompt_ids`. 'first-segment' means only the first segment is conditioned on `prompt_ids`. 'all-segments' means each segment is conditioned on `prompt_ids`. Make sure to enable `condition_on_prev_tokens` for 'all-segments'.
Defaults to 'first-... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
generation using sampling. For long-form transcription, temperature fallback can be activated by passing
a list of float values such as (0.0, 0.2, 0.4, 0.6, 0.8, 1.0). As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), this can help to improve
performance.
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
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