text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if prompt_ids is not None and generation_config.prompt_condition_type == "all-segments":
prev_ids = prompt_ids
else:
one_tensor = torch.ones((cur_bsz, 1), device=device, dtype=torch.long)
prev_ids = prev_start_of_text * one_tensor[0] if prev_start_of_text is n... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
kwargs["decoder_attention_mask"] = decoder_input_ids != generation_config.pad_token_id
elif prompt_ids is not None:
prev_tokens = prompt_ids[None].repeat(decoder_input_ids.shape[0], 1)
decoder_input_ids = torch.cat([prev_tokens, decoder_input_ids], dim=-1)
# make sure `"decod... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
def _set_max_new_tokens_and_length(self, config, decoder_input_ids, generation_config):
max_new_tokens = generation_config.max_new_tokens if generation_config.max_new_tokens is not None else 0
if max_new_tokens + decoder_input_ids.shape[-1] > self.config.max_target_positions:
raise ValueErro... | 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 |
num_initial_tokens = min(config.max_target_positions // 2 - 1, decoder_input_ids.shape[-1] - 1)
# Make sure we don't get larger than `max_length`
if generation_config.max_length is not None and generation_config.max_new_tokens is None:
max_length = min(generation_config.max_length + num_ini... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _retrieve_compression_ratio(tokens, vocab_size):
"""Compute byte length of zlib compressed token bytes vs. byte length of raw token bytes"""
length = int(math.log2(vocab_size) / 8) + 1
token_bytes = b"".join([t.to_bytes(length, "little") for t in tokens.tolist()])
c... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# retrieve logprob of selected tokens and sum
# don't remove the eos token logprob! it counts in avg_logprob calculation in the original implementation
sum_logprobs = sum(logprobs[i][tokens[i]] for i in range(logprobs.shape[0]))
avg_logprobs = sum_logprobs / len(tokens)
return avg_logpr... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
@staticmethod
def _retrieve_segment(
seek_sequence,
seek_outputs,
time_offset,
timestamp_begin,
seek_num_frames,
time_precision,
time_precision_features,
input_stride,
prev_idx,
idx,
return_token_timestamps,
decoder_inpu... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
# If whisper predicted a "end of segment" via a timestep token, let's go ever each
# "end of segment" prediction and slice the decoding into segments accordingly
if len(timestamp_segment_indices) > 0:
# if the output contains two consecutive timestamp tokens
slices = timestamp_se... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
last_slice = 0
# Add each segment to list of all segments
for i, current_slice in enumerate(slices):
is_last_slice = i == len(slices) - 1
sliced_tokens = seek_sequence[last_slice:current_slice]
start_timestamp_pos = sliced_tokens[0] - timestamp_beg... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
"tokens": sliced_tokens,
"idxs": (idx_offset + last_slice, idx_offset + current_slice),
"result": seek_outputs[idx],
}
)
if return_token_timestamps:
segments[-1]["token_timestamps"] = (
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if single_timestamp_ending:
# single timestamp at the end means no speech after the last timestamp.
segment_offset = seek_num_frames[prev_idx]
else:
# otherwise, ignore the unfinished segment and seek to the last timestamp
# here we throw away ... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
if timestamps.numel() > 0 and timestamps[-1] != timestamp_begin:
# no consecutive timestamps but it has a timestamp; use the last one.
last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(
torch.float32 if device.type == "mps" else torch.float64
... | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
return segments, segment_offset | 9,920 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/generation_whisper.py |
class WhisperFeatureExtractor(SequenceFeatureExtractor):
r"""
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 ... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
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`, *opt... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
def __init__(
self,
feature_size=80,
sampling_rate=16000,
hop_length=160,
chunk_length=30,
n_fft=400,
padding_value=0.0,
return_attention_mask=False, # pad inputs to max length with silence token (zero) and no attention mask
**kwargs,
):
... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
sampling_rate=sampling_rate,
norm="slaney",
mel_scale="slaney",
) | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
def _np_extract_fbank_features(self, waveform_batch: np.array, device: str) -> np.ndarray:
"""
Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch
implementation with 1e-5 tolerance.
"""
if device != "cpu":
raise Va... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
log_mel="log10",
)
log_spec = log_spec[:, :-1]
log_spec = np.maximum(log_spec, log_spec.max() - 8.0)
log_spec = (log_spec + 4.0) / 4.0
log_spec_batch.append(log_spec)
log_spec_batch = np.array(log_spec_batch)
return log_spec_batch | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
def _torch_extract_fbank_features(self, waveform: np.array, device: str = "cpu") -> np.ndarray:
"""
Compute the log-mel spectrogram of the audio using PyTorch's GPU-accelerated STFT implementation with batching,
yielding results similar to cpu computing with 1e-5 tolerance.
"""
w... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
log_spec = torch.clamp(mel_spec, min=1e-10).log10()
if waveform.dim() == 2:
max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0]
log_spec = torch.maximum(log_spec, max_val - 8.0)
else:
log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
for vector, length in zip(input_values, attention_mask.sum(-1)):
normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
if length < normed_slice.shape[0]:
normed_slice[length:] = padding_value
normed_input_values.appe... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
truncation: bool = True,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
return_attention_mask: Optional[bool] = None,
... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
Args:
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
values, a list of numpy arrays or a list of list of float values. Must be mono channel... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mas... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
sampling_rate (`int`, *optional*):
The sampling rate at which the `raw_speech` input was sampled. It is strongly re... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
Specifies the device for computation of the log-mel spectrogram of audio signals in the
`_torch_extract_fbank_features` method. (e.g., "cpu", "cuda")
return_token_timestamps (`bool`, *optional*, defaults to `None`):
Whether or not to return the number of frames of the input r... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
f" sampling rate of {self.sampling_rate}. Please make sure that ... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tu... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
# convert into correct format for padding
padded_inputs = self.pad(
batched_speech,
padding=padding,
max_length=max_length if max_length else self.n_samples,
truncation=truncation,
pad_to_multiple_of=pad_to_multiple_of,
return_attention_ma... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
extract_fbank_features = (
self._torch_extract_fbank_features if is_torch_available() else self._np_extract_fbank_features
)
input_features = extract_fbank_features(input_features[0], device)
if isinstance(input_features[0], List):
padded_inputs["input_features"] = [np.a... | 9,921 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/feature_extraction_whisper.py |
class FlaxWhisperAttention(nn.Module):
config: WhisperConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_di... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_target_positions), dtype="bool"), dtype="bool"
)
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: ... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask =... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
def _split_heads(self, hidden_state) -> jnp.ndarray:
return hidden_state.reshape(hidd... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
# detect if we're initializing by absence of existing cache data.
is_initialized = self.has_variable("cache", "cached_key")
cached_key = self.variable("cache", "cac... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
)
attention_mask = combine_masks(pad_mask, attention_mask) | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
return key, value, attention_mask | 9,922 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperEncoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 | 9,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.... | 9,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) | 9,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
hidden_s... | 9,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
return outputs | 9,923 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperEncoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoder... | 9,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_state... | 9,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
for encoder_layer in self.layers:
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (... | 9,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
) | 9,924 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperDecoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=sel... | 9,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
self.encoder_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtyp... | 9,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic:... | 9,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_stat... | 9,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs | 9,925 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperDecoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
if self.gradient_checkpointing:
FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoder... | 9,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
out... | 9,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probabi... | 9,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions]
if not return_dict:
return tuple(v for v in outputs if v is not None)
... | 9,926 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperEncoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.conv1 = nn.Conv(
self.config.d_model,
kernel_size=(3,),
padding=1,
kernel_init=jax.nn.init... | 9,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
self.embed_positions = nn.Embed(
self.config.max_source_positions,
self.config.d_model,
dtype=self.dtype,
embedding_init=sinusoidal_embedding_init,
)
self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05)
def __call__(
self,
... | 9,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
input_features = input_features.transpose(0, 2, 1)
hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False)
hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False)
embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions))
# ... | 9,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states... | 9,927 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperDecoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype)
self.embed_positions = nn.Embed(self.config... | 9,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: jnp.ndarray,
position_ids: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
... | 9,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
outputs = self.layers(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidde... | 9,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
) | 9,928 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
)
... | 9,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
decoder_attention_mask: jnp.ndarray,
decoder_position_ids: jnp.ndarray,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
... | 9,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
output_attentions=output_attentions,
output_hidden_states=output_hidden... | 9,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder | 9,929 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
config_class = WhisperConfig
base_model_prefix: str = "model"
main_input_name = "input_features"
module_class: nn.Module = None
def __init__(
self,
config: WhisperConfig,
input_shape: Tuple[int] = None,
seed: int... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_features = jnp.zeros(input_shape, dtype="f4")
input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)
decoder_input_ids = jnp.zero... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freez... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->Whisper
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the bat... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.sh... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
@add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig)
def encode(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processo... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def _encoder_forward(module, input_features, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_features, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=WhisperConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> input_features = ... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
positi... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# prepare decoder inputs
if decoder_position_ids is None:
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
batch_size, sequence_length = decoder_input_ids.shape
... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_positio... | 9,930 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxWhisperModule | 9,931 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperForConditionalGenerationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False
def setup(self) -> None:
self.model = FlaxWhisperModule(
config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_c... | 9,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
def __call__(
self,
input_features,
decoder_input_ids,
decoder_attention_mask: jnp.ndarray = None,
decoder_position_ids: jnp.ndarray = None,
position_ids: jnp.ndarray = None,
attention_mask: jnp.ndarray = None,
output_attentions: bool = False,
outp... | 9,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if self.config.tie_word_embeddings:
shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not retu... | 9,932 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForConditionalGenerationModule
dtype: jnp.dtype = jnp.float32
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_clas... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-t... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_st... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxWhisperAttention module
if past_key_values:
inputs["c... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
if self.config.tie_word_embeddings:
shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states... | 9,933 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_flax_whisper.py |
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