text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
suffer from a lot of repetition. The unwanted repetition can be reduced by injecting more randomness by increasing the temperature. If `compression_ratio_threshold` is defined
make sure that `temperature` is a list of values. A common value for `compression_ratio_threshold` is 1.35.
As s... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
can be improved by injecting more randomness by increasing the temperature. If `logprob_threshold` is defined
make sure that `temperature` is a list of values. A common value for `logprob_threshold` is -1.0.
As shown in the [the Whisper paper](https://cdn.openai.com/papers/whisper.pdf), ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
The number of frames a single segment is made of. If not defined, `num_segment_frames` defaults to the model's stride
times the maximum input length.
attention_mask (`torch.Tensor`, *optional*):
`attention_mask` needs to be passed when doing long-form transcription using a ba... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return_segments (`bool`, *optional*, defaults to `False`):
Whether to additionally return a list of all segments. Note that this option can only be enabled
when doing long-form transcription.
return_dict_in_generate (`bool`, *optional*, defaults to `False`):
W... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
that only one call to [`~generation.GenerationMixin.generate`] is made and therefore decoder input token ids and eos token ids are returned.
kwargs (`Dict[str, Any]`, *optional*):
Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
A:
- [`~utils.ModelOutput`] when `return_dict_in_generate=True` and (`return_timestamps=False` or `force_unique_generate_call=True`), including the decoder input ids and end of sequence id.
- `Dict[str, Any]` when (`return_dict_in_generate=True` and `return_timestamps=True`) or `return_s... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
`segments` is a list of lists (one list per batch element) of `segment`.
A `segment` is a dictionary with keys `start`, `end`, `tokens`, `idxs`, and `result`.
- `start`: the start timestamp of the segment.
- `end`: the end timestamp of the segment.
- `toke... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
- *Longform transcription*: To transcribe or translate audios longer than 30 seconds, process the audio files without truncation and pass all mel features at once to generate. It is necessary to set `return_timestamps=True`.
Indeed, long-form transcription uses a sequential algorithm based on timestamps predict... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
>>> # load audios > 30 seconds
>>> ds = load_dataset("distil-whisper/meanwhile", "default")["test"]
>>> # resample to 16kHz
>>> ds = ds.cast_column("audio", Audio(sampling_rate=16000))
>>> # take first 8 audios and retrieve array
>>> audio = ds[:8]["audio"]
>>> audio = [x... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)
>>> transcription[0] | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
" Folks, if you watch the show, you know, I spent a lot of time right over there. Patiently and astutely scrutinizing the boxwood and mahogany chest set of the day's biggest stories developing the central headline pawns, definitely maneuvering an oso topical night to F6, fainting a classic Sicilian, nade door variation... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
toothless drifters to the godless bughouse blitz of tournament that is my segment. Meanwhile." | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
``` | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
- *Shortform transcription*: If passed mel input features are <= 30 seconds, there are two possibilities:
- `return_timestamps=False`: the whole audio will be transcribed with a single call to GenerationMixin's [`~generation.GenerationMixin.generate`].
- `return_timestamps=True`: the audio will ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
>>> generated_ids = model.generate(inputs=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```
"""
# 0. de... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 2. set global generate variables
input_stride = self.model.encoder.conv1.stride[0] * self.model.encoder.conv2.stride[0]
num_segment_frames = input_stride * self.config.max_source_positions
batch_size, total_input_frames = self._retrieve_total_input_frames(
input_features=input_feat... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 3. Make sure generation config is correctly set
# Make sure the generation config is correctly set depending on whether timestamps are to be returned or not
return_dict_in_generate = self._set_return_outputs(
return_dict_in_generate=return_dict_in_generate,
return_token_timesta... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
generation_config=generation_config,
logprob_threshold=logprob_threshold,
compression_ratio_threshold=compression_ratio_threshold,
no_speech_threshold=no_speech_threshold,
condition_on_prev_tokens=condition_on_prev_tokens,
)
self._set_prompt_condition_type... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# pass self.config for backward compatibility
init_tokens = self._retrieve_init_tokens(
input_features,
batch_size=batch_size,
generation_config=generation_config,
config=self.config,
num_segment_frames=num_segment_frames,
kwargs=kwargs,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 3. Retrieve logits processors
device = kwargs["encoder_outputs"][0].device if "encoder_outputs" in kwargs else input_features.device
begin_index = init_tokens.shape[1]
num_beams = kwargs.get(
"num_beams",
generation_config.num_beams
if hasattr(generation_con... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 4 Set and retrieve global generation variables
self._set_condition_on_prev_tokens(
condition_on_prev_tokens=condition_on_prev_tokens, generation_config=generation_config
)
temperatures = [temperature] if not isinstance(temperature, (list, tuple)) else temperature
temperatu... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 5 Prepare running variables, list for generation
num_return_sequences = generation_config.num_return_sequences
(
batch_idx_map,
cur_bsz,
input_features,
seek,
max_frames,
init_tokens,
do_condition_on_prev_tokens,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
current_segments = self._prepare_segments(
prompt_ids=prompt_ids,
batch_size=cur_bsz,
generation_config=generation_config,
)
# 5bis speculative decoding: ensure the assistant model does only one call to generate and therefore returns decoder input token ids and eos to... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if force_unique_generate_call is None:
if hasattr(generation_config, "force_unique_generate_call"):
force_unique_generate_call = generation_config.force_unique_generate_call
elif hasattr(self.generation_config, "force_unique_generate_call"):
force_unique_generate_... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6 Transcribe audio until we reach the end of all input audios
while (seek < max_frames).any():
# 6.1 NOTE: When in longform transcription mode and batch size > 1 we need to dynamically reduce the batch size during the loop
# in case one audio finished earlier than another one. Thus, we... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
seek_num_frames = (max_frames - seek).clamp(max=num_segment_frames) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6.2 cut out next 30s segment from input features
segment_input = self._get_input_segment(
input_features=input_features,
seek=seek,
seek_num_frames=seek_num_frames,
num_segment_frames=num_segment_frames,
cur_bsz=cur_bsz,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
decoder_input_ids, kwargs = self._prepare_decoder_input_ids(
cur_bsz=cur_bsz,
init_tokens=init_tokens,
current_segments=current_segments,
batch_idx_map=batch_idx_map,
do_condition_on_prev_tokens=do_condition_on_prev_tokens,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6.5 Set current `begin_index` for all logit processors
if logits_processor is not None:
for proc in logits_processor:
if hasattr(proc, "set_begin_index"):
proc.set_begin_index(decoder_input_ids.shape[-1]) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6.6 Run generate with fallback
(
seek_sequences,
seek_outputs,
should_skip,
do_condition_on_prev_tokens,
model_output_type,
) = self.generate_with_fallback(
segment_input=segment_input,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
is_shortform=is_shortform,
batch_size=batch_size,
attention_mask=attention_mask,
kwargs=kwargs,
) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6.7 In every generated sequence, split by timestamp tokens and extract segments
for i, seek_sequence in enumerate(seek_sequences):
prev_i = batch_idx_map[i]
if should_skip[i]:
seek[prev_i] += seek_num_frames[prev_i]
continue
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
seek[prev_i] += segment_offset
current_segments[prev_i] += segments
if force_unique_generate_call:
break
# 7. Once all segments are added to the list of all segments, called `current_segments`, we extract the predicted
# output tokens from the list of dicts... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# if return_dict_in_generate=True and we forced a unique call to generate or return_timestamps=False, meaning we are sure only one call to generate has been made,
# -> we can return a ModelOutput
# otherwise, return_dict_in_generate is applied in the 'result' of each segment in final_segments
if... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
for i in range(len(outputs.encoder_attentions))
)
if hasattr(outputs, "encoder_hidden_states") and outputs.encoder_hidden_states is not None:
outputs.encoder_hidden_states = tuple(
outputs.encoder_hidden_states[i][::num_return_sequences]
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
padded_outputs = _pad_to_max_length(
current_segments=final_segments,
pad_token_id=generation_config.pad_token_id,
device=self.device,
padding_side="right",
return_token_timestamps=return_token_timestamps,
force_unique_generate_call=force_unique_ge... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if return_token_timestamps:
sequences, token_timestamps = padded_outputs
outputs = {
"sequences": sequences,
"token_timestamps": token_timestamps,
}
else:
sequences = padded_outputs
outputs = {
"sequences... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# 6.6 Batch generate current chunk
seek_sequence_list = [None for _ in range(cur_bsz)]
seek_outputs_list = [None for _ in range(cur_bsz)]
needs_fallback = [False for _ in range(cur_bsz)]
should_skip = [False for _ in range(cur_bsz)]
fallback_index_map = list(range(cur_bsz))
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
generate_kwargs = copy.copy(kwargs)
for key in ["do_sample", "temperature", "num_beams"]:
if key in generate_kwargs:
del generate_kwargs[key] | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
cur_bsz = decoder_input_ids.shape[0]
if generation_config.cache_implementation == "static" and cur_bsz < batch_size:
segment_input = F.pad(segment_input, (0, 0, 0, 0, 0, batch_size - cur_bsz), value=0)
decoder_input_ids = F.pad(
decoder_input_ids, (0, 0, 0... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
seek_outputs = super().generate(
segment_input,
generation_config=generation_config,
logits_processor=logits_processor,
stopping_criteria=stopping_criteria,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
synced_gpus=sync... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if cur_bsz < batch_size:
seek_sequences = seek_sequences[:cur_bsz]
seek_outputs = seek_outputs[:cur_bsz]
# 6.7 Extract cut sequences from every sequence and check if fallback should be applied
# Loop over each decoded audio individually as each decoding can be of... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
for i, seek_sequence in enumerate(seek_sequences):
# remove all padding tokens, except for the eos token
if seek_sequence[-1] == generation_config.pad_token_id:
num_paddings = (seek_sequence == generation_config.pad_token_id).sum()
if generation_co... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# check which sequences in batch need fallback & which should be skipped
needs_fallback[i], should_skip[i] = self._need_fallback(
seek_sequence,
seek_outputs,
i,
logits_processor,
generation_config,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if needs_fallback[i]:
new_fallback_index_map.append(fallback_index_map[i])
new_segment_input.append(segment_input[i])
new_decoder_input_ids.append(decoder_input_ids[i])
if "decoder_attention_mask" in kwargs:
new_deco... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# if we're still in the loop, make sure that decoder_input_ids and segment inputs are tensors
decoder_input_ids = torch.stack(new_decoder_input_ids)
segment_input = torch.stack(new_segment_input)
if "decoder_attention_mask" in kwargs:
kwargs["decoder_attention_mask"] ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return current_segments
def _postprocess_outputs(
self,
seek_outputs,
decoder_input_ids,
return_token_timestamps,
generation_config,
is_shortform,
):
# remove all previously passed decoder input ids
# should happen only if it is the first generate... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def split_by_batch_index(values, key, batch_idx, is_shortform, beam_indices=None):
if beam_indices is not None and key == "scores":
return [v[beam_idx].cpu() for (v, beam_idx) in zip(values, beam_indices[batch_idx][: len(values)])]
if key in ["scores", "encoder_attentions", "enco... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
layer_past_key_values = []
for cache_cls in [values.self_attention_cache, values.cross_attention_cache]:
for v in [cache_cls.key_cache, cache_cls.value_cache]:
layer_past_key_values.append(v[layer_idx][batch_idx][None].cpu())
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return tuple(all_past_key_values) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return values[batch_idx].cpu()
sequence_tokens = seek_outputs["sequences"][:, start_idx:]
seek_outputs = [
{
k: split_by_batch_index(v, k, i, is_shortform, beam_indices=seek_outputs.get("beam_indices"))
for k, v in seek_outputs.items()
}
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _stack_split_outputs(self, seek_outputs, model_output_type, device, kwargs):
# Stack back seek_outputs tensors after splitting them with the split_by_batch_index method
outputs = {}
for key in seek_outputs[0].keys():
if key in ["sequences", "beam_indices", "token_timestamps"]:
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
torch.stack([v[key][i][j] for v in seek_outputs]).squeeze(1).to(device)
for j in range(len(seek_outputs[0][key][0]))
)
for i in range(len(seek_outputs[0][key]))
)
elif key == "past_key_values":
past_key_value_typ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
outputs[key] = None | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
token_timestamps = outputs.get("token_timestamps", None)
if token_timestamps is not None:
model_output_type = dict
return model_output_type(**outputs)
def _need_fallback(
self,
seek_sequence,
seek_outputs,
index,
logits_processor,
generat... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if generation_config.logprob_threshold is not None:
if hasattr(seek_outputs[0], "sequences_scores"):
logprobs = [s["sequences_scores"] for s in seek_outputs][index]
else:
scores = seek_outputs[index]["scores"]
logprobs = self._retrieve_avg_logprobs... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return needs_fallback, should_skip | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _expand_variables_for_generation(
self, input_features, seek, max_frames, init_tokens, batch_size, condition_on_prev_tokens, generation_config
):
if generation_config.num_return_sequences is not None and generation_config.num_return_sequences > 1:
batch_idx_map = list(range(batch_siz... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
batch_idx_map = list(range(cur_bsz))
do_condition_on_prev_tokens = [condition_on_prev_tokens for _ in range(cur_bsz)] | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return (
batch_idx_map,
cur_bsz,
input_features,
seek,
max_frames,
init_tokens,
do_condition_on_prev_tokens,
)
@staticmethod
def _setup_no_speech_detection(logits_processor, segment_input, decoder_input_ids, kwargs):
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if "encoder_outputs" in kwargs:
encoder_outputs_shape = (
kwargs["encoder_outputs"][0].shape
if isinstance(kwargs["encoder_outputs"], BaseModelOutput)
else kwargs["encoder_outputs"].shape
)
return encoder_outputs_shape[0], encoder_outpu... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _maybe_warn_unused_inputs(
condition_on_prev_tokens,
temperature,
compression_ratio_threshold,
logprob_threshold,
no_speech_threshold,
total_input_frames,
):
warning_prefix = (
f"Audio input consists of only {total_input_frame... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if no_speech_threshold is not None:
logger.warning(warning_prefix.format(f"no_speech_threshold is set to {no_speech_threshold}"))
@staticmethod
def _set_return_outputs(return_dict_in_generate, return_token_timestamps, logprob_threshold, generation_config):
if return_dict_in_generate is None... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _set_return_timestamps(self, return_timestamps, is_shortform, generation_config):
if return_timestamps is None and hasattr(generation_config, "return_timestamps"):
return_timestamps = generation_config.return_timestamps
if not is_shortform:
if return_timestamps is False:
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if return_timestamps and not hasattr(generation_config, "no_timestamps_token_id"):
raise ValueError(
"You are trying to return timestamps, but the generation config is not properly set. "
"Make sure to initialize the generation config with the correct attributes that are need... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if hasattr(generation_config, "no_timestamps_token_id"):
timestamp_begin = generation_config.no_timestamps_token_id + 1
else:
# BC for models missing the `no_timestamps_token_id` in the generation config when generating short-form with no timestamps
# We set the timestamp beg... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _set_language_and_task(language, task, is_multilingual, generation_config):
if is_multilingual is not None:
if not hasattr(generation_config, "is_multilingual"):
raise ValueError(
"The generation config is outdated and is thus not compatible ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if hasattr(generation_config, "is_multilingual") and not generation_config.is_multilingual:
if task is not None or language is not None:
raise ValueError(
"Cannot specify `task` or `language` for an English-only model. If the model is intended to be "
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if task is not None:
if not hasattr(generation_config, "task_to_id"):
raise ValueError(
"The generation config is outdated and is thus not compatible with the `task` argument "
"to `generate`. Either set the task using the `forced_decoder_ids` in the m... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _retrieve_init_tokens(self, input_features, batch_size, generation_config, config, num_segment_frames, kwargs):
def replace_or_add(lst: List[int], num: int, itr: Iterator[int]):
"""short function to replace num with a itr in lst"""
found = any(i in lst for i in itr)
if fo... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def language_to_id(language: str) -> int:
language = language.lower()
if language in generation_config.lang_to_id.keys():
language_token = language
elif language in TO_LANGUAGE_CODE.keys():
language_token = f"<|{TO_LANGUAGE_CODE[language]}|>"
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
"(You should just add it to the generation config)"
) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return generation_config.lang_to_id[language_token]
task = getattr(generation_config, "task", None)
language = getattr(generation_config, "language", None)
forced_decoder_ids = generation_config.forced_decoder_ids
if forced_decoder_ids is not None:
if language is None and t... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if forced_decoder_ids is not None and task is not None:
logger.warning_once(
f"You have passed task={task}, but also have set `forced_decoder_ids` to {forced_decoder_ids} which creates a conflict. `forced_decoder_ids` will be ignored in favor of task={task}."
)
forced... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
init_tokens = [generation_config.decoder_start_token_id]
if forced_decoder_ids is not None and forced_decoder_ids[0][0] == 1:
i = 1
while len(forced_decoder_ids) > 0 and forced_decoder_ids[0][0] == i:
init_tokens += [forced_decoder_ids[0][1]]
forced_decode... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Make sure language is a list of strings of the correct length
if isinstance(language, (list, tuple)):
if any(l is None for l in language):
raise TypeError(
"Expected `language` to be `None`, a single string (e.g. `'en'`), or a list of strings with length equal t... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Separate init_tokens for each language
init_tokens = [copy.copy(init_tokens) for _ in languages] | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Update init_tokens with languages
lang_ids = None
if language is not None:
lang_ids = [language_to_id(l) for l in languages]
elif hasattr(generation_config, "lang_to_id") and is_lang_id_undefined:
# language is not defined or intentially set to `None` to trigger languag... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# Update init_tokens with task
for i in range(len(init_tokens)):
if task is not None:
if task in TASK_IDS:
init_tokens[i].append(generation_config.task_to_id[generation_config.task])
task_id = generation_config.task_to_id[generation_config.task... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# if task is defined it'll overwrite task ids that might have already been defined via the generation_config
replace_or_add(init_tokens[i], task_id, generation_config.task_to_id.values())
else:
raise ValueError(f"The `{task}`task is not supported. The task should ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if (
not generation_config.return_timestamps
and hasattr(generation_config, "no_timestamps_token_id")
and init_tokens[i][-1] != generation_config.no_timestamps_token_id
):
init_tokens[i].append(generation_config.no_timestamps_token_id)
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def detect_language(
self,
input_features: Optional[torch.FloatTensor] = None,
encoder_outputs: Optional[Union[torch.FloatTensor, BaseModelOutput]] = None,
generation_config: Optional[GenerationConfig] = None,
num_segment_frames: int = 3000,
) -> torch.Tensor:
"""
... | 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 |
`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. Used in the cross-attention of the decoder.
generation_config (`~generation.GenerationConfig`, *optional*):
Th... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
num_segment_frames (`int`, *optional*, defaults to 3000):
The number of log-mel frames the model expects | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
Return:
A `torch.LongTensor` representing the detected language ids.
"""
if input_features is None and encoder_outputs is None:
raise ValueError("You have to specify either `input_features` or `encoder_outputs`")
elif input_features is not None and encoder_outputs is not ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
generation_config = generation_config or self.generation_config
decoder_input_ids = (
torch.ones((batch_size, 1), device=self.device, dtype=torch.long)
* generation_config.decoder_start_token_id
)
with torch.no_grad():
logits = self(**inputs, decoder_input_id... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _check_decoder_input_ids(kwargs):
decoder_input_ids = kwargs.get("decoder_input_ids", None)
assistant_model = kwargs.get("assistant_model", None)
if decoder_input_ids is not None and assistant_model is not None:
raise ValueError(
"Passing `decode... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _set_num_frames(return_token_timestamps, generation_config, kwargs):
if return_token_timestamps:
if getattr(generation_config, "task", None) == "translate":
logger.warning("Token-level timestamps may not be reliable for task 'translate'.")
if not has... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _set_thresholds_and_condition(
generation_config,
logprob_threshold,
compression_ratio_threshold,
no_speech_threshold,
condition_on_prev_tokens,
):
generation_config.logprob_threshold = (
logprob_threshold
if logprob_thres... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
else getattr(generation_config, "condition_on_prev_tokens", None)
) | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _set_prompt_condition_type(generation_config, prompt_condition_type):
allowed_cond_types = ["first-segment", "all-segments"]
# default to "first-segment"
prompt_condition_type = prompt_condition_type or allowed_cond_types[0]
if prompt_condition_type not in allowed... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _set_condition_on_prev_tokens(condition_on_prev_tokens, generation_config):
condition_on_prev_tokens = (
condition_on_prev_tokens
if condition_on_prev_tokens is not None
else getattr(generation_config, "condition_on_prev_tokens", False)
)
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _retrieve_max_frames_and_seek(batch_size, attention_mask, total_input_frames, is_shortform):
if batch_size > 1 and not is_shortform and attention_mask is None:
raise ValueError(
"When doing batched long-form audio transcription, make sure to pass an `attention_m... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _retrieve_logit_processors(self, generation_config, logits_processor, begin_index, num_beams, device):
if generation_config.return_timestamps is True:
timestamp_processor = WhisperTimeStampLogitsProcessor(generation_config, begin_index=begin_index)
logits_processor = (
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if generation_config.begin_suppress_tokens is not None:
begin_suppress_processor = SuppressTokensAtBeginLogitsProcessor(
generation_config.begin_suppress_tokens, begin_index=begin_index, device=device
)
logits_processor = (
[begin_suppress_processor]
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return logits_processor
@staticmethod
def _maybe_reduce_batch(input_features, seek, max_frames, cur_bsz, batch_idx_map):
prev_bsz = cur_bsz
new_batch_idx_map = []
for i in range(prev_bsz):
prev_i = batch_idx_map[i]
if seek[prev_i] >= max_frames[prev_i]:
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
segment_input = []
for i in range(cur_bsz):
prev_i = batch_idx_map[i]
segment_input_slice = input_features[i : i + 1, :, seek[prev_i] : seek[prev_i] + seek_num_frames[prev_i]]
if segment_input_slice.shape[-1] < num_segment_frames:
# pad to 3000 if necessary
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _prepare_decoder_input_ids(
cur_bsz,
init_tokens,
current_segments,
batch_idx_map,
do_condition_on_prev_tokens,
prompt_ids,
generation_config,
config,
device,
suppress_tokens,
timestamp_begin,
kwargs,
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if any(do_condition_on_prev_tokens) and len(current_segments[0]) > 0:
# according to https://github.com/openai/whisper/blob/e58f28804528831904c3b6f2c0e473f346223433/whisper/decoding.py#L609
active_segments = [current_segments[i] if do_condition_on_prev_tokens[i] else None for i in batch_idx_map]... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
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