Upload seamless_communication/inference/generator.py with huggingface_hub

seamless_communication/inference/generator.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# MIT_LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+from typing import List, Optional, Tuple
+
+import torch
+from fairseq2.data import SequenceData, StringLike
+from fairseq2.data.text import TextTokenizer
+from fairseq2.generation import (
+    BeamSearchSeq2SeqGenerator,
+    Seq2SeqGenerator,
+    SequenceToTextConverter,
+    StepProcessor,
+)
+from fairseq2.nn.padding import (
+    PaddingMask,
+    apply_padding_mask,
+    get_seqs_and_padding_mask,
+    pad_seqs,
+)
+from fairseq2.nn.utils.module import infer_device
+from torch import Tensor
+
+from seamless_communication.models.unity.model import (
+    UnitYModel,
+    UnitYT2UModel,
+    UnitYX2TModel,
+)
+from seamless_communication.models.unity.unit_tokenizer import (
+    UnitTokenDecoder,
+    UnitTokenizer,
+)
+
+
+def remove_consecutive_repeated_ngrams(
+    sequence: List[int], min_size: int = 1, max_size: int = 40
+) -> List[int]:
+    assert 1 <= min_size <= max_size
+    drop_idx = set()  # indices that will be dropped from the sequence
+
+    # start from the beginning, check if an ngram of size k (for k=max..min) is
+    # followed by its copy, if so delete the first one, and start over after
+    # the deleted ngram.
+    start = 0
+    while start < len(sequence):
+        for k in range(max_size, min_size - 1, -1):
+            if sequence[start : start + k] == sequence[start + k : start + k + k]:
+                drop_idx |= set(range(start, start + k))
+                start += k - 1  # assumes repeating subsequences don't overlap
+                break
+        start += 1
+    return [token for idx, token in enumerate(sequence) if idx not in drop_idx]
+
+
+@dataclass
+class SequenceGeneratorOptions:
+    """Holds the options to pass to a sequence generator."""
+
+    beam_size: int = 5
+    """The beam size."""
+
+    soft_max_seq_len: Tuple[int, int] = (1, 200)
+    """The terms ``a`` and ``b`` of ``ax + b`` where ``x`` is the source
+    sequence length. The generated sequences (including prefix sequence) will
+    have the maximum length of ``min(hard_max_seq_len, ax + b)``. See also
+    ``hard_max_seq_len``."""
+
+    hard_max_seq_len: int = 1024
+    """The hard limit on maximum length of generated sequences."""
+
+    step_processor: Optional[StepProcessor] = None
+    """The processor called at each generation step."""
+
+    unk_penalty: float = 0.0
+    """The UNK symbol penalty, where values less than 0 produce more UNKs;
+    values greater than 0 produce fewer UNKs."""
+
+    len_penalty: float = 1.0
+    """The length penalty, where values less than 1.0 favor shorter
+    sequences; values greater than 1.0 favor longer sequences."""
+
+
+class UnitYGenerator:
+    """Generates text translations and speech units from a UnitY model."""
+
+    model: UnitYModel
+    s2t_converter: SequenceToTextConverter
+    t2t_converter: Optional[SequenceToTextConverter]
+    unit_decoder: Optional[UnitTokenDecoder]
+    unit_prefix_indices: Optional[Tensor]
+    unit_generator: Optional[Seq2SeqGenerator]
+
+    def __init__(
+        self,
+        model: UnitYModel,
+        text_tokenizer: TextTokenizer,
+        target_lang: str,
+        unit_tokenizer: Optional[UnitTokenizer] = None,
+        text_opts: Optional[SequenceGeneratorOptions] = None,
+        unit_opts: Optional[SequenceGeneratorOptions] = None,
+    ) -> None:
+        """
+        :param model:
+            The UnitY model to use for generation.
+        :param text_tokenizer:
+            The text tokenizer to use.
+        :param unit_tokenizer:
+            The unit tokenizer to use.
+        :param target_lang:
+            The target language.
+        :param text_generator_opts:
+            The options to pass to the underlying text :class:`Seq2SeqGenerator`.
+        :param unit_generator_opts:
+            The options to pass to the underlying unit :class:`Seq2SeqGenerator`.
+        """
+        model.eval()
+
+        self.model = model
+
+        if text_opts is None:
+            text_opts = SequenceGeneratorOptions()
+
+        if model.text_decoder is None:
+            raise ValueError(
+                "`UnitYGenerator` requires a text decoder, but the current UnitY model does not have one."
+            )
+        assert model.text_decoder_frontend is not None
+        assert model.final_proj is not None
+
+        s2t_model = UnitYX2TModel(
+            encoder_frontend=model.speech_encoder_frontend,
+            encoder=model.speech_encoder,
+            decoder_frontend=model.text_decoder_frontend,
+            decoder=model.text_decoder,
+            final_proj=model.final_proj,
+            target_vocab_info=model.target_vocab_info,
+        )
+
+        step_processors = []
+        if text_opts.step_processor is not None:
+            step_processors.append(text_opts.step_processor)
+
+        generator = BeamSearchSeq2SeqGenerator(
+            s2t_model,
+            beam_size=text_opts.beam_size,
+            max_gen_len=text_opts.soft_max_seq_len,
+            max_seq_len=text_opts.hard_max_seq_len,
+            echo_prompt=True,
+            step_processors=step_processors,
+            unk_penalty=text_opts.unk_penalty,
+            len_penalty=text_opts.len_penalty,
+        )
+        self.s2t_converter = SequenceToTextConverter(
+            generator, text_tokenizer, "translation", target_lang
+        )
+
+        if model.text_encoder is None:
+            self.t2t_generator = None
+        else:
+            assert model.text_encoder_frontend is not None
+            assert model.text_encoder is not None
+            t2t_model = UnitYX2TModel(
+                encoder_frontend=model.text_encoder_frontend,
+                encoder=model.text_encoder,
+                decoder_frontend=model.text_decoder_frontend,
+                decoder=model.text_decoder,
+                final_proj=model.final_proj,
+                target_vocab_info=model.target_vocab_info,
+            )
+            generator = BeamSearchSeq2SeqGenerator(
+                t2t_model,
+                beam_size=text_opts.beam_size,
+                max_gen_len=text_opts.soft_max_seq_len,
+                max_seq_len=text_opts.hard_max_seq_len,
+                echo_prompt=True,
+                step_processors=step_processors,
+                unk_penalty=text_opts.unk_penalty,
+                len_penalty=text_opts.len_penalty,
+            )
+            self.t2t_converter = SequenceToTextConverter(
+                generator, text_tokenizer, "translation", target_lang
+            )
+
+        self.unit_generator = None
+        self.unit_decoder = None
+        # Set up unit generator.
+        if unit_tokenizer is not None:
+            if model.t2u_model is None:
+                raise ValueError(
+                    "`model` does not have a T2U sub-model when `unit_tokenizer` is not None."
+                )
+
+            self.unit_decoder = unit_tokenizer.create_decoder()
+
+            unit_encoder = unit_tokenizer.create_encoder(
+                lang=target_lang, device=infer_device(model.t2u_model)
+            )
+
+            self.unit_prefix_indices = unit_encoder.prefix_indices
+
+            if isinstance(self.model.t2u_model, UnitYT2UModel):
+                if unit_opts is None:
+                    # Speech sequences are typically much longer than text sequences.
+                    unit_opts = SequenceGeneratorOptions(
+                        soft_max_seq_len=(25, 50), hard_max_seq_len=5000
+                    )
+
+                step_processors = []
+                if unit_opts.step_processor is not None:
+                    step_processors.append(unit_opts.step_processor)
+
+                self.unit_generator = BeamSearchSeq2SeqGenerator(
+                    self.model.t2u_model,
+                    beam_size=unit_opts.beam_size,
+                    max_gen_len=unit_opts.soft_max_seq_len,
+                    max_seq_len=unit_opts.hard_max_seq_len,
+                    echo_prompt=True,
+                    step_processors=step_processors,
+                    unk_penalty=unit_opts.unk_penalty,
+                    len_penalty=unit_opts.len_penalty,
+                )
+
+    @torch.inference_mode()
+    def __call__(
+        self,
+        source_seqs: Tensor,
+        source_padding_mask: Optional[PaddingMask],
+        input_modality: str = "speech",
+        output_modality: str = "speech",
+        ngram_filtering: bool = False,
+        duration_factor: float = 1.0,
+        prosody_encoder_input: Optional[SequenceData] = None,
+    ) -> Tuple[List[StringLike], Optional[Tensor]]:
+        """
+        :param source_seqs:
+            The source sequences to use for generation. *Shape:* :math:`(N,S,*)`,
+            where :math:`N` is the batch size, :math:`S` is the sequence length,
+            and :math:`*` is any number of sequence-specific dimensions
+            including none.
+        :param source_padding_mask:
+            The padding mask of ``source_seqs``. *Shape:* :math:`(N,S)`, where
+            :math:`N` is the batch size and :math:`S` is the sequence length.
+        :param input_modality:
+            The type of modality to encode.
+        :param output_modality:
+            The type of modality to decode.
+        :param ngram_filtering:
+            If True, removes consecutive repeated ngrams
+            from the decoded unit output.
+
+        :returns:
+            - The output of the text generator.
+            - The output of the unit generator.
+        """
+
+        if input_modality == "speech":
+            texts, text_gen_output = self.s2t_converter.batch_convert(
+                source_seqs, source_padding_mask
+            )
+        elif input_modality == "text":
+            if self.t2t_converter is None:
+                raise ValueError(
+                    "Please set `use_text_encoder` to `True` in your model config to encode text."
+                )
+            texts, text_gen_output = self.t2t_converter.batch_convert(
+                source_seqs, source_padding_mask
+            )
+        else:
+            raise ValueError(f"Unsupported input_modality: {input_modality}")
+
+        # We skip T2U when we only need to output text.
+        if output_modality == "text":
+            return texts, None
+
+        assert self.model.target_vocab_info.pad_idx is not None
+
+        text_seq_list = [h[0].seq for h in text_gen_output.hypotheses]
+
+        text_seqs, text_padding_mask = pad_seqs(
+            text_seq_list, self.model.target_vocab_info.pad_idx
+        )
+
+        # Manually trim the final EOS token to be consistent with fairseq.
+        text_seqs = text_seqs[:, :-1]
+
+        if text_padding_mask is not None:
+            text_padding_mask = text_padding_mask.trim(1)
+
+        # Use the output of the text generator to compute the decoder output.
+        decoder_output, decoder_padding_mask = self.model.decode(
+            text_seqs,
+            text_padding_mask,
+            text_gen_output.encoder_output,
+            text_gen_output.encoder_padding_mask,
+        )
+
+        assert self.model.t2u_model is not None
+        assert self.unit_decoder is not None
+
+        unit_gen_output = None
+        prosody_encoder_out = None
+        if self.model.prosody_encoder_model is not None:
+            assert prosody_encoder_input is not None
+            prosody_input_seqs, prosody_padding_mask = get_seqs_and_padding_mask(
+                prosody_encoder_input
+            )
+            prosody_encoder_out = self.model.prosody_encoder_model(
+                prosody_input_seqs,
+                prosody_padding_mask,
+            ).unsqueeze(1)
+
+        if isinstance(self.model.t2u_model, UnitYT2UModel):
+            assert self.unit_generator is not None
+            assert self.unit_prefix_indices is not None
+
+            # (S_pre) -> (N, S_pre)
+            prefix_seqs = self.unit_prefix_indices.expand(decoder_output.size(0), -1)
+
+            unit_gen_output = self.unit_generator(
+                source_seqs=decoder_output,
+                source_padding_mask=decoder_padding_mask,
+                prompt_seqs=prefix_seqs,
+                prompt_padding_mask=None,
+            )
+
+            assert self.model.t2u_model.target_vocab_info.pad_idx is not None
+
+            unit_seq_list = [h[0].seq for h in unit_gen_output.hypotheses]
+
+            unit_seqs, _ = pad_seqs(
+                unit_seq_list, self.model.t2u_model.target_vocab_info.pad_idx
+            )
+        else:
+            t2u_model_output, decoder_padding_mask, _ = self.model.t2u_model(
+                text_decoder_output=decoder_output,
+                text_decoder_padding_mask=decoder_padding_mask,
+                text_seqs=text_seqs,
+                duration_factor=duration_factor,
+                film_cond_emb=prosody_encoder_out,
+            )
+            # (B, S_unit, V_unit)
+            unit_seqs = t2u_model_output.logits.argmax(dim=2)
+            # Apply the padding mask to the generated units.
+            unit_seqs = apply_padding_mask(
+                unit_seqs, decoder_padding_mask, t2u_model_output.vocab_info.pad_idx
+            )
+
+        # Convert to speech units.
+        units = self.unit_decoder(unit_seqs)
+
+        # ngram-filtering doesn't apply to NAR unit decoding.
+        if ngram_filtering and isinstance(self.model.t2u_model, UnitYT2UModel):
+            if units.size(0) > 1:
+                raise NotImplementedError(
+                    "unit ngram_filtering is not implemented for batch_size > 1."
+                )
+            arr = remove_consecutive_repeated_ngrams(units[0].tolist())
+            units = torch.tensor(arr).to(units).unsqueeze(0)
+
+        return texts, units