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Batch translation for text inputs #187

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Batch translation for text inputs #187

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31 changes: 27 additions & 4 deletions src/seamless_communication/models/inference/translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,11 @@
# LICENSE file in the root directory of this source tree.

from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
from typing import Callable, Dict, List, Optional, Tuple, Union, Any

import torch
import torch.nn as nn
from torch.nn.functional import pad as pad_tensor
from fairseq2.assets.card import AssetCard
from fairseq2.data import Collater
from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
Expand Down Expand Up @@ -146,6 +147,16 @@ def get_prediction(
ngram_filtering=ngram_filtering,
)

def batch_tensors(self, tensors: List[Tensor], pad_value: Any) -> Tensor:
padding_size = max(tensor.shape[0] for tensor in tensors)
dims = len(tensors[0].shape)
padded_tensors = []
for tensor in tensors:
padding = [0] * 2 * dims
padding[-1] = padding_size - tensor.shape[0]
padded_tensors.append(pad_tensor(tensor, padding, "constant", pad_value))
return torch.stack([tensor for tensor in padded_tensors], dim=0)

def get_modalities_from_task(self, task: Task) -> Tuple[Modality, Modality]:
if task == Task.S2ST:
return Modality.SPEECH, Modality.SPEECH
Expand All @@ -160,7 +171,7 @@ def get_modalities_from_task(self, task: Task) -> Tuple[Modality, Modality]:
@torch.inference_mode()
def predict(
self,
input: Union[str, Tensor],
input: Union[str, List[str], Tensor],
task_str: str,
tgt_lang: str,
src_lang: Optional[str] = None,
Expand Down Expand Up @@ -220,7 +231,16 @@ def predict(
self.token_encoder = self.text_tokenizer.create_encoder(
task="translation", lang=src_lang, mode="source", device=self.device
)
src = self.collate(self.token_encoder(text))

if isinstance(text, str):
src = self.collate(self.token_encoder(text))
else:
collated = [self.collate(self.token_encoder(t)) for t in text]

src = {
'seqs': self.batch_tensors([item['seqs'].squeeze() for item in collated], self.text_tokenizer.vocab_info.pad_idx),
'seq_lens': torch.cat([item['seq_lens'] for item in collated])
}

result = self.get_prediction(
self.model,
Expand All @@ -240,7 +260,10 @@ def predict(
text_out = result[0]
unit_out = result[1]
if output_modality == Modality.TEXT:
return text_out.sentences[0], None, None
if len(text_out.sentences) == 0:
return text_out.sentences[0], None, None
else:
return text_out.sentences, None, None
else:
units = unit_out.units[:, 1:][0].cpu().numpy().tolist()
wav_out = self.vocoder(units, tgt_lang, spkr, dur_prediction=True)
Expand Down