kotoba_whisper.py

import requests
from typing import Union, Optional, Dict, List, Any
from collections import defaultdict

import torch
import numpy as np

from transformers.pipelines.audio_utils import ffmpeg_read
from transformers.pipelines.automatic_speech_recognition import AutomaticSpeechRecognitionPipeline, chunk_iter
from transformers.utils import is_torchaudio_available
from transformers.modeling_utils import PreTrainedModel
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
from pyannote.audio import Pipeline
from pyannote.core.annotation import Annotation
from punctuators.models import PunctCapSegModelONNX
from diarizers import SegmentationModel


class Punctuator:

    ja_punctuations = ["!", "?", "、", "。"]

    def __init__(self, model: str = "pcs_47lang"):
        self.punctuation_model = PunctCapSegModelONNX.from_pretrained(model)

    def punctuate(self, pipeline_chunk: List[Dict[str, Any]]) -> List[Dict[str, Any]]:

        def validate_punctuation(raw: str, punctuated: str):
            if 'unk' in punctuated.lower() or any(p in raw for p in self.ja_punctuations):
                return raw
            if punctuated.count("。") > 1:
                ind = punctuated.rfind("。")
                punctuated = punctuated.replace("。", "")
                punctuated = punctuated[:ind] + "。" + punctuated[ind:]
            return punctuated

        text_edit = self.punctuation_model.infer([c['text'] for c in pipeline_chunk])
        return [
            {
                'timestamp': c['timestamp'],
                'speaker': c['speaker'],
                'text': validate_punctuation(c['text'], "".join(e))
            } for c, e in zip(pipeline_chunk, text_edit)
        ]


class SpeakerDiarization:

    def __init__(self,
                 device: torch.device,
                 model_id: str = "pyannote/speaker-diarization-3.1",
                 model_id_diarizers: Optional[str] = None):
        self.device = device
        self.pipeline = Pipeline.from_pretrained(model_id)
        self.pipeline = self.pipeline.to(self.device)
        if model_id_diarizers:
            self.pipeline._segmentation.model = SegmentationModel().from_pretrained(
                model_id_diarizers
            ).to_pyannote_model().to(self.device)

    def __call__(self,
                 audio: Union[torch.Tensor, np.ndarray],
                 sampling_rate: int,
                 num_speakers: Optional[int] = None,
                 min_speakers: Optional[int] = None,
                 max_speakers: Optional[int] = None) -> Annotation:
        if sampling_rate is None:
            raise ValueError("sampling_rate must be provided")
        if type(audio) is np.ndarray:
            audio = torch.as_tensor(audio)
        audio = torch.as_tensor(audio, dtype=torch.float32)
        if len(audio.shape) == 1:
            audio = audio.unsqueeze(0)
        elif len(audio.shape) > 3:
            raise ValueError("audio shape must be (channel, time)")
        audio = {"waveform": audio.to(self.device), "sample_rate": sampling_rate}
        output = self.pipeline(audio, num_speakers=num_speakers, min_speakers=min_speakers, max_speakers=max_speakers)
        return output


class KotobaWhisperPipeline(AutomaticSpeechRecognitionPipeline):

    def __init__(self,
                 model: "PreTrainedModel",
                 model_pyannote: str = "pyannote/speaker-diarization-3.1",
                 model_diarizers: Optional[str] = "diarizers-community/speaker-segmentation-fine-tuned-callhome-jpn",
                 feature_extractor: Union["SequenceFeatureExtractor", str] = None,
                 tokenizer: Optional[PreTrainedTokenizer] = None,
                 device: Union[int, "torch.device"] = None,
                 device_pyannote: Union[int, "torch.device"] = None,
                 torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
                 **kwargs):
        self.type = "seq2seq_whisper"
        if device is None:
            device = "cpu"
        if device_pyannote is None:
            device_pyannote = device
        if type(device_pyannote) is str:
            device_pyannote = torch.device(device_pyannote)
        self.model_speaker_diarization = SpeakerDiarization(
            device=device_pyannote,
            model_id=model_pyannote,
            model_id_diarizers=model_diarizers
        )
        self.punctuator = None
        super().__init__(
            model=model,
            feature_extractor=feature_extractor,
            tokenizer=tokenizer,
            device=device,
            torch_dtype=torch_dtype,
            **kwargs
        )

    def _sanitize_parameters(self,
                             chunk_length_s=None,
                             stride_length_s=None,
                             ignore_warning=None,
                             decoder_kwargs=None,
                             return_timestamps=None,
                             return_language=None,
                             generate_kwargs=None,
                             max_new_tokens=None,
                             add_punctuation: bool =False,
                             return_unique_speaker: bool =True,
                             num_speakers: Optional[int] = None,
                             min_speakers: Optional[int] = None,
                             max_speakers: Optional[int] = None):
        # No parameters on this pipeline right now
        preprocess_params = {}
        if chunk_length_s is not None:
            preprocess_params["chunk_length_s"] = chunk_length_s
        if stride_length_s is not None:
            preprocess_params["stride_length_s"] = stride_length_s

        forward_params = defaultdict(dict)
        if max_new_tokens is not None:
            forward_params["max_new_tokens"] = max_new_tokens
        if generate_kwargs is not None:
            if max_new_tokens is not None and "max_new_tokens" in generate_kwargs:
                raise ValueError(
                    "`max_new_tokens` is defined both as an argument and inside `generate_kwargs` argument, please use"
                    " only 1 version"
                )
            forward_params.update(generate_kwargs)

        postprocess_params = {}
        if decoder_kwargs is not None:
            postprocess_params["decoder_kwargs"] = decoder_kwargs
        if return_timestamps is not None:
            # Check whether we have a valid setting for return_timestamps and throw an error before we perform a forward pass
            if self.type == "seq2seq" and return_timestamps:
                raise ValueError("We cannot return_timestamps yet on non-CTC models apart from Whisper!")
            if self.type == "ctc_with_lm" and return_timestamps != "word":
                raise ValueError("CTC with LM can only predict word level timestamps, set `return_timestamps='word'`")
            if self.type == "ctc" and return_timestamps not in ["char", "word"]:
                raise ValueError(
                    "CTC can either predict character level timestamps, or word level timestamps. "
                    "Set `return_timestamps='char'` or `return_timestamps='word'` as required."
                )
            if self.type == "seq2seq_whisper" and return_timestamps == "char":
                raise ValueError(
                    "Whisper cannot return `char` timestamps, only word level or segment level timestamps. "
                    "Use `return_timestamps='word'` or `return_timestamps=True` respectively."
                )
            forward_params["return_timestamps"] = return_timestamps
            postprocess_params["return_timestamps"] = return_timestamps
        if return_language is not None:
            if self.type != "seq2seq_whisper":
                raise ValueError("Only Whisper can return language for now.")
            postprocess_params["return_language"] = return_language
        postprocess_params["return_language"] = return_language
        postprocess_params["add_punctuation"] = add_punctuation
        postprocess_params["return_unique_speaker"] = return_unique_speaker
        postprocess_params["num_speakers"] = num_speakers
        postprocess_params["min_speakers"] = min_speakers
        postprocess_params["max_speakers"] = max_speakers
        return preprocess_params, forward_params, postprocess_params

    def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
        if isinstance(inputs, str):
            if inputs.startswith("http://") or inputs.startswith("https://"):
                # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                # like http_huggingface_co.png
                inputs = requests.get(inputs).content
            else:
                with open(inputs, "rb") as f:
                    inputs = f.read()

        if isinstance(inputs, bytes):
            inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)

        stride = None
        extra = {}
        if isinstance(inputs, dict):
            stride = inputs.pop("stride", None)
            # Accepting `"array"` which is the key defined in `datasets` for
            # better integration
            if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                raise ValueError(
                    "When passing a dictionary to AutomaticSpeechRecognitionPipeline, the dict needs to contain a "
                    '"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '
                    "containing the sampling_rate associated with that array"
                )

            _inputs = inputs.pop("raw", None)
            if _inputs is None:
                # Remove path which will not be used from `datasets`.
                inputs.pop("path", None)
                _inputs = inputs.pop("array", None)
            in_sampling_rate = inputs.pop("sampling_rate")
            extra = inputs
            inputs = _inputs
            if in_sampling_rate != self.feature_extractor.sampling_rate:
                if is_torchaudio_available():
                    from torchaudio import functional as F
                else:
                    raise ImportError(
                        "torchaudio is required to resample audio samples in AutomaticSpeechRecognitionPipeline. "
                        "The torchaudio package can be installed through: `pip install torchaudio`."
                    )

                inputs = F.resample(
                    torch.from_numpy(inputs), in_sampling_rate, self.feature_extractor.sampling_rate
                ).numpy()
                ratio = self.feature_extractor.sampling_rate / in_sampling_rate
            else:
                ratio = 1
            if stride is not None:
                if stride[0] + stride[1] > inputs.shape[0]:
                    raise ValueError("Stride is too large for input")

                # Stride needs to get the chunk length here, it's going to get
                # swallowed by the `feature_extractor` later, and then batching
                # can add extra data in the inputs, so we need to keep track
                # of the original length in the stride so we can cut properly.
                stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
        if not isinstance(inputs, np.ndarray):
            raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
        if len(inputs.shape) != 1:
            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")

        if chunk_length_s:
            if stride_length_s is None:
                stride_length_s = chunk_length_s / 6

            if isinstance(stride_length_s, (int, float)):
                stride_length_s = [stride_length_s, stride_length_s]

            # XXX: Carefuly, this variable will not exist in `seq2seq` setting.
            # Currently chunking is not possible at this level for `seq2seq` so
            # it's ok.
            align_to = getattr(self.model.config, "inputs_to_logits_ratio", 1)
            chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
            stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to)
            stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to)

            if chunk_len < stride_left + stride_right:
                raise ValueError("Chunk length must be superior to stride length")

            for item in chunk_iter(
                    inputs, self.feature_extractor, chunk_len, stride_left, stride_right, self.torch_dtype
            ):
                item["audio_array"] = inputs
                yield item
        else:
            if inputs.shape[0] > self.feature_extractor.n_samples:
                processed = self.feature_extractor(
                    inputs,
                    sampling_rate=self.feature_extractor.sampling_rate,
                    truncation=False,
                    padding="longest",
                    return_tensors="pt",
                )
            else:
                processed = self.feature_extractor(
                    inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
                )

            if self.torch_dtype is not None:
                processed = processed.to(dtype=self.torch_dtype)
            if stride is not None:
                processed["stride"] = stride
            yield {"is_last": True, "audio_array": inputs, **processed, **extra}

    def _forward(self, model_inputs, **generate_kwargs):
        attention_mask = model_inputs.pop("attention_mask", None)
        stride = model_inputs.pop("stride", None)
        is_last = model_inputs.pop("is_last")
        audio_array = model_inputs.pop("audio_array")
        encoder = self.model.get_encoder()
        # Consume values so we can let extra information flow freely through
        # the pipeline (important for `partial` in microphone)
        if "input_features" in model_inputs:
            inputs = model_inputs.pop("input_features")
        elif "input_values" in model_inputs:
            inputs = model_inputs.pop("input_values")
        else:
            raise ValueError(
                "Seq2Seq speech recognition model requires either a "
                f"`input_features` or `input_values` key, but only has {model_inputs.keys()}"
            )

        # custom processing for Whisper timestamps and word-level timestamps
        generate_kwargs["return_timestamps"] = True
        if inputs.shape[-1] > self.feature_extractor.nb_max_frames:
            generate_kwargs["input_features"] = inputs
        else:
            generate_kwargs["encoder_outputs"] = encoder(inputs, attention_mask=attention_mask)

        tokens = self.model.generate(attention_mask=attention_mask, **generate_kwargs)
        # whisper longform generation stores timestamps in "segments"
        out = {"tokens": tokens}
        if self.type == "seq2seq_whisper":
            if stride is not None:
                out["stride"] = stride

        # Leftover
        extra = model_inputs
        return {"is_last": is_last, "audio_array": audio_array, **out, **extra}

    def postprocess(self,
                    model_outputs,
                    decoder_kwargs: Optional[Dict] = None,
                    return_language=None,
                    add_punctuation: bool = False,
                    return_unique_speaker: bool = True,
                    num_speakers: Optional[int] = None,
                    min_speakers: Optional[int] = None,
                    max_speakers: Optional[int] = None,
                    *args,
                    **kwargs):
        assert len(model_outputs) > 0
        outputs = super().postprocess(
            model_outputs=model_outputs,
            decoder_kwargs=decoder_kwargs,
            return_timestamps=True,
            return_language=return_language
        )
        audio_array = outputs.pop("audio_array")[0]
        sd = self.model_speaker_diarization(
            audio_array,
            num_speakers=num_speakers,
            min_speakers=min_speakers,
            max_speakers=max_speakers,
            sampling_rate=self.feature_extractor.sampling_rate
        )
        diarization_result = {s: [[i.start, i.end] for i in sd.label_timeline(s)] for s in sd.labels()}
        timelines = sd.get_timeline()

        pointer_ts = 0
        pointer_chunk = 0
        new_chunks = []
        while True:
            if pointer_ts == len(timelines):
                ts = timelines[-1]
                for chunk in outputs["chunks"][pointer_chunk:]:
                    chunk["speaker"] = sd.get_labels(ts)
                    new_chunks.append(chunk)
                break
            if pointer_chunk == len(outputs["chunks"]):
                break
            ts = timelines[pointer_ts]

            chunk = outputs["chunks"][pointer_chunk]
            if "speaker" not in chunk:
                chunk["speaker"] = []

            start, end = chunk["timestamp"]
            if ts.end <= start:
                pointer_ts += 1
            elif end <= ts.start:
                if len(chunk["speaker"]) == 0:
                    chunk["speaker"] += list(sd.get_labels(ts))
                new_chunks.append(chunk)
                pointer_chunk += 1
            else:
                chunk["speaker"] += list(sd.get_labels(ts))
                if ts.end >= end:
                    new_chunks.append(chunk)
                    pointer_chunk += 1
                else:
                    pointer_ts += 1
        for i in new_chunks:
            if "speaker" in i:
                if return_unique_speaker:
                    i["speaker"] = [i["speaker"][0]]
                else:
                    i["speaker"] = list(set(i["speaker"]))
            else:
                i["speaker"] = []
        outputs["chunks"] = new_chunks
        if add_punctuation:
            if self.punctuator is None:
                self.punctuator = Punctuator()
            outputs["chunks"] = self.punctuator.punctuate(outputs["chunks"])
        outputs["text"] = "".join([c["text"] for c in outputs["chunks"]])
        outputs["speakers"] = sd.labels()
        speakers = []
        for s in outputs["speakers"]:
            chunk_s = [c for c in outputs["chunks"] if s in c["speaker"]]
            if len(chunk_s) != 0:
                outputs[f"chunks/{s}"] = chunk_s
                outputs[f"text/{s}"] = "".join([c["text"] for c in outputs["chunks"] if s in c["speaker"]])
                speakers.append(s)
        outputs["speakers"] = speakers
        outputs["diarization_result"] = diarization_result
        return outputs