funasr_detach/auto/auto_model.py

import json
import time
import copy
import torch
import random
import string
import logging
import os.path
import numpy as np
from tqdm import tqdm

from funasr_detach.register import tables
from funasr_detach.utils.load_utils import load_bytes
from funasr_detach.download.file import download_from_url
from funasr_detach.download.download_from_hub import download_model
from funasr_detach.utils.vad_utils import slice_padding_audio_samples
from funasr_detach.train_utils.set_all_random_seed import set_all_random_seed
from funasr_detach.train_utils.load_pretrained_model import load_pretrained_model
from funasr_detach.utils.load_utils import load_audio_text_image_video
from funasr_detach.utils.timestamp_tools import timestamp_sentence
from funasr_detach.models.campplus.utils import sv_chunk, postprocess, distribute_spk

try:
    from funasr_detach.models.campplus.cluster_backend import ClusterBackend
except:
    print("If you want to use the speaker diarization, please `pip install hdbscan`")


def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
    """

    :param input:
    :param input_len:
    :param data_type:
    :param frontend:
    :return:
    """
    data_list = []
    key_list = []
    filelist = [".scp", ".txt", ".json", ".jsonl"]

    chars = string.ascii_letters + string.digits
    if isinstance(data_in, str) and data_in.startswith("http"):  # url
        data_in = download_from_url(data_in)
    if isinstance(data_in, str) and os.path.exists(
        data_in
    ):  # wav_path; filelist: wav.scp, file.jsonl;text.txt;
        _, file_extension = os.path.splitext(data_in)
        file_extension = file_extension.lower()
        if file_extension in filelist:  # filelist: wav.scp, file.jsonl;text.txt;
            with open(data_in, encoding="utf-8") as fin:
                for line in fin:
                    key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
                    if data_in.endswith(
                        ".jsonl"
                    ):  # file.jsonl: json.dumps({"source": data})
                        lines = json.loads(line.strip())
                        data = lines["source"]
                        key = data["key"] if "key" in data else key
                    else:  # filelist, wav.scp, text.txt: id \t data or data
                        lines = line.strip().split(maxsplit=1)
                        data = lines[1] if len(lines) > 1 else lines[0]
                        key = lines[0] if len(lines) > 1 else key

                    data_list.append(data)
                    key_list.append(key)
        else:
            key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
            data_list = [data_in]
            key_list = [key]
    elif isinstance(data_in, (list, tuple)):
        if data_type is not None and isinstance(
            data_type, (list, tuple)
        ):  # mutiple inputs
            data_list_tmp = []
            for data_in_i, data_type_i in zip(data_in, data_type):
                key_list, data_list_i = prepare_data_iterator(
                    data_in=data_in_i, data_type=data_type_i
                )
                data_list_tmp.append(data_list_i)
            data_list = []
            for item in zip(*data_list_tmp):
                data_list.append(item)
        else:
            # [audio sample point, fbank, text]
            data_list = data_in
            key_list = [
                "rand_key_" + "".join(random.choice(chars) for _ in range(13))
                for _ in range(len(data_in))
            ]
    else:  # raw text; audio sample point, fbank; bytes
        if isinstance(data_in, bytes):  # audio bytes
            data_in = load_bytes(data_in)
        if key is None:
            key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
        data_list = [data_in]
        key_list = [key]

    return key_list, data_list


class AutoModel:

    def __init__(self, **kwargs):
        if not kwargs.get("disable_log", False):
            tables.print()

        model, kwargs = self.build_model(**kwargs)

        # if vad_model is not None, build vad model else None
        vad_model = kwargs.get("vad_model", None)
        vad_kwargs = kwargs.get("vad_model_revision", None)
        if vad_model is not None:
            logging.info("Building VAD model.")
            vad_kwargs = {
                "model": vad_model,
                "model_revision": vad_kwargs,
                "device": kwargs["device"],
            }
            vad_model, vad_kwargs = self.build_model(**vad_kwargs)

        # if punc_model is not None, build punc model else None
        punc_model = kwargs.get("punc_model", None)
        punc_kwargs = kwargs.get("punc_model_revision", None)
        if punc_model is not None:
            logging.info("Building punc model.")
            punc_kwargs = {
                "model": punc_model,
                "model_revision": punc_kwargs,
                "device": kwargs["device"],
            }
            punc_model, punc_kwargs = self.build_model(**punc_kwargs)

        # if spk_model is not None, build spk model else None
        spk_model = kwargs.get("spk_model", None)
        spk_kwargs = kwargs.get("spk_model_revision", None)
        if spk_model is not None:
            logging.info("Building SPK model.")
            spk_kwargs = {
                "model": spk_model,
                "model_revision": spk_kwargs,
                "device": kwargs["device"],
            }
            spk_model, spk_kwargs = self.build_model(**spk_kwargs)
            self.cb_model = ClusterBackend().to(kwargs["device"])
            spk_mode = kwargs.get("spk_mode", "punc_segment")
            if spk_mode not in ["default", "vad_segment", "punc_segment"]:
                logging.error(
                    "spk_mode should be one of default, vad_segment and punc_segment."
                )
            self.spk_mode = spk_mode

        self.kwargs = kwargs
        self.model = model
        self.vad_model = vad_model
        self.vad_kwargs = vad_kwargs
        self.punc_model = punc_model
        self.punc_kwargs = punc_kwargs
        self.spk_model = spk_model
        self.spk_kwargs = spk_kwargs
        self.model_path = kwargs.get("model_path")

    def build_model(self, **kwargs):
        assert "model" in kwargs
        if "model_conf" not in kwargs:
            logging.info(
                "download models from model hub: {}".format(
                    kwargs.get("model_hub", "ms")
                )
            )
            kwargs = download_model(**kwargs)

        set_all_random_seed(kwargs.get("seed", 0))

        device = kwargs.get("device", "cuda")
        if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
            device = "cpu"
            kwargs["batch_size"] = 1
        kwargs["device"] = device

        if kwargs.get("ncpu", None):
            torch.set_num_threads(kwargs.get("ncpu"))

        # build tokenizer
        tokenizer = kwargs.get("tokenizer", None)
        if tokenizer is not None:
            tokenizer_class = tables.tokenizer_classes.get(tokenizer)
            tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
            kwargs["tokenizer"] = tokenizer
            kwargs["token_list"] = tokenizer.token_list
            vocab_size = len(tokenizer.token_list)
        else:
            vocab_size = -1

        # build frontend
        frontend = kwargs.get("frontend", None)
        if frontend is not None:
            frontend_class = tables.frontend_classes.get(frontend)
            frontend = frontend_class(**kwargs["frontend_conf"])
            kwargs["frontend"] = frontend
            kwargs["input_size"] = frontend.output_size()

        # build model
        model_class = tables.model_classes.get(kwargs["model"])
        model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size)

        model.to(device)

        # init_param
        init_param = kwargs.get("init_param", None)
        if init_param is not None:
            logging.info(f"Loading pretrained params from {init_param}")
            load_pretrained_model(
                model=model,
                path=init_param,
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                oss_bucket=kwargs.get("oss_bucket", None),
                scope_map=kwargs.get("scope_map", None),
                excludes=kwargs.get("excludes", None),
            )

        return model, kwargs

    def __call__(self, *args, **cfg):
        kwargs = self.kwargs
        kwargs.update(cfg)
        res = self.model(*args, kwargs)
        return res

    def generate(self, input, input_len=None, **cfg):
        if self.vad_model is None:
            return self.inference(input, input_len=input_len, **cfg)

        else:
            return self.inference_with_vad(input, input_len=input_len, **cfg)

    def inference(
        self, input, input_len=None, model=None, kwargs=None, key=None, **cfg
    ):
        kwargs = self.kwargs if kwargs is None else kwargs
        kwargs.update(cfg)
        model = self.model if model is None else model
        model = model.cuda()
        model.eval()

        batch_size = kwargs.get("batch_size", 1)
        # if kwargs.get("device", "cpu") == "cpu":
        #     batch_size = 1

        key_list, data_list = prepare_data_iterator(
            input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
        )

        speed_stats = {}
        asr_result_list = []
        num_samples = len(data_list)
        disable_pbar = kwargs.get("disable_pbar", False)
        pbar = (
            tqdm(colour="blue", total=num_samples, dynamic_ncols=True)
            if not disable_pbar
            else None
        )
        time_speech_total = 0.0
        time_escape_total = 0.0
        for beg_idx in range(0, num_samples, batch_size):
            end_idx = min(num_samples, beg_idx + batch_size)
            data_batch = data_list[beg_idx:end_idx]
            key_batch = key_list[beg_idx:end_idx]
            batch = {"data_in": data_batch, "key": key_batch}
            if (end_idx - beg_idx) == 1 and kwargs.get(
                "data_type", None
            ) == "fbank":  # fbank
                batch["data_in"] = data_batch[0]
                batch["data_lengths"] = input_len

            time1 = time.perf_counter()
            with torch.no_grad():
                results, meta_data = model.inference(**batch, **kwargs)
            time2 = time.perf_counter()

            asr_result_list.extend(results)

            # batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
            batch_data_time = meta_data.get("batch_data_time", -1)
            time_escape = time2 - time1
            speed_stats["load_data"] = meta_data.get("load_data", 0.0)
            speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
            speed_stats["forward"] = f"{time_escape:0.3f}"
            speed_stats["batch_size"] = f"{len(results)}"
            speed_stats["time_cost"] = f"{(time_escape)}"
            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
            description = f"{speed_stats}, "
            if pbar:
                pbar.update(1)
                pbar.set_description(description)
            time_speech_total += batch_data_time
            time_escape_total += time_escape

        if pbar:
            # pbar.update(1)
            pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
        torch.cuda.empty_cache()
        return asr_result_list

    def inference_with_vad(self, input, input_len=None, **cfg):

        # step.1: compute the vad model
        self.vad_kwargs.update(cfg)
        beg_vad = time.time()
        res = self.inference(
            input,
            input_len=input_len,
            model=self.vad_model,
            kwargs=self.vad_kwargs,
            **cfg,
        )
        end_vad = time.time()
        print(f"time cost vad: {end_vad - beg_vad:0.3f}")

        # step.2 compute asr model
        model = self.model
        kwargs = self.kwargs
        kwargs.update(cfg)
        batch_size = int(kwargs.get("batch_size_s", 300)) * 1000
        batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60)) * 1000
        kwargs["batch_size"] = batch_size

        key_list, data_list = prepare_data_iterator(
            input, input_len=input_len, data_type=kwargs.get("data_type", None)
        )
        results_ret_list = []
        time_speech_total_all_samples = 1e-6

        beg_total = time.time()
        pbar_total = tqdm(colour="red", total=len(res), dynamic_ncols=True)
        for i in range(len(res)):
            key = res[i]["key"]
            vadsegments = res[i]["value"]
            input_i = data_list[i]
            speech = load_audio_text_image_video(
                input_i, fs=kwargs["frontend"].fs, audio_fs=kwargs.get("fs", 16000)
            )
            speech_lengths = len(speech)
            n = len(vadsegments)
            data_with_index = [(vadsegments[i], i) for i in range(n)]
            sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
            results_sorted = []

            if not len(sorted_data):
                logging.info("decoding, utt: {}, empty speech".format(key))
                continue

            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
                batch_size = max(
                    batch_size, sorted_data[0][0][1] - sorted_data[0][0][0]
                )

            batch_size_ms_cum = 0
            beg_idx = 0
            beg_asr_total = time.time()
            time_speech_total_per_sample = speech_lengths / 16000
            time_speech_total_all_samples += time_speech_total_per_sample

            all_segments = []
            for j, _ in enumerate(range(0, n)):
                # pbar_sample.update(1)
                batch_size_ms_cum += sorted_data[j][0][1] - sorted_data[j][0][0]
                if (
                    j < n - 1
                    and (
                        batch_size_ms_cum
                        + sorted_data[j + 1][0][1]
                        - sorted_data[j + 1][0][0]
                    )
                    < batch_size
                    and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0])
                    < batch_size_threshold_ms
                ):
                    continue
                batch_size_ms_cum = 0
                end_idx = j + 1
                speech_j, speech_lengths_j = slice_padding_audio_samples(
                    speech, speech_lengths, sorted_data[beg_idx:end_idx]
                )
                results = self.inference(
                    speech_j,
                    input_len=None,
                    model=model,
                    kwargs=kwargs,
                    disable_pbar=True,
                    **cfg,
                )
                if self.spk_model is not None:
                    # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
                    for _b in range(len(speech_j)):
                        vad_segments = [
                            [
                                sorted_data[beg_idx:end_idx][_b][0][0] / 1000.0,
                                sorted_data[beg_idx:end_idx][_b][0][1] / 1000.0,
                                np.array(speech_j[_b]),
                            ]
                        ]
                        segments = sv_chunk(vad_segments)
                        all_segments.extend(segments)
                        speech_b = [i[2] for i in segments]
                        spk_res = self.inference(
                            speech_b,
                            input_len=None,
                            model=self.spk_model,
                            kwargs=kwargs,
                            disable_pbar=True,
                            **cfg,
                        )
                        results[_b]["spk_embedding"] = spk_res[0]["spk_embedding"]
                beg_idx = end_idx
                if len(results) < 1:
                    continue
                results_sorted.extend(results)

            restored_data = [0] * n
            for j in range(n):
                index = sorted_data[j][1]
                restored_data[index] = results_sorted[j]
            result = {}

            # results combine for texts, timestamps, speaker embeddings and others
            # TODO: rewrite for clean code
            for j in range(n):
                for k, v in restored_data[j].items():
                    if k.startswith("timestamp"):
                        if k not in result:
                            result[k] = []
                        for t in restored_data[j][k]:
                            t[0] += vadsegments[j][0]
                            t[1] += vadsegments[j][0]
                        result[k].extend(restored_data[j][k])
                    elif k == "spk_embedding":
                        if k not in result:
                            result[k] = restored_data[j][k]
                        else:
                            result[k] = torch.cat(
                                [result[k], restored_data[j][k]], dim=0
                            )
                    elif "text" in k:
                        if k not in result:
                            result[k] = restored_data[j][k]
                        else:
                            result[k] += " " + restored_data[j][k]
                    else:
                        if k not in result:
                            result[k] = restored_data[j][k]
                        else:
                            result[k] += restored_data[j][k]

            return_raw_text = kwargs.get("return_raw_text", False)
            # step.3 compute punc model
            if self.punc_model is not None:
                self.punc_kwargs.update(cfg)
                punc_res = self.inference(
                    result["text"],
                    model=self.punc_model,
                    kwargs=self.punc_kwargs,
                    disable_pbar=True,
                    **cfg,
                )
                raw_text = copy.copy(result["text"])
                if return_raw_text:
                    result["raw_text"] = raw_text
                result["text"] = punc_res[0]["text"]
            else:
                raw_text = None

            # speaker embedding cluster after resorted
            if self.spk_model is not None and kwargs.get("return_spk_res", True):
                if raw_text is None:
                    logging.error("Missing punc_model, which is required by spk_model.")
                all_segments = sorted(all_segments, key=lambda x: x[0])
                spk_embedding = result["spk_embedding"]
                labels = self.cb_model(
                    spk_embedding.cpu(), oracle_num=kwargs.get("preset_spk_num", None)
                )
                # del result['spk_embedding']
                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                if self.spk_mode == "vad_segment":  # recover sentence_list
                    sentence_list = []
                    for res, vadsegment in zip(restored_data, vadsegments):
                        if "timestamp" not in res:
                            logging.error(
                                "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
                                           and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                                           can predict timestamp, and speaker diarization relies on timestamps."
                            )
                        sentence_list.append(
                            {
                                "start": vadsegment[0],
                                "end": vadsegment[1],
                                "sentence": res["text"],
                                "timestamp": res["timestamp"],
                            }
                        )
                elif self.spk_mode == "punc_segment":
                    if "timestamp" not in result:
                        logging.error(
                            "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
                                       and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                                       can predict timestamp, and speaker diarization relies on timestamps."
                        )
                    sentence_list = timestamp_sentence(
                        punc_res[0]["punc_array"],
                        result["timestamp"],
                        raw_text,
                        return_raw_text=return_raw_text,
                    )
                distribute_spk(sentence_list, sv_output)
                result["sentence_info"] = sentence_list
            elif kwargs.get("sentence_timestamp", False):
                sentence_list = timestamp_sentence(
                    punc_res[0]["punc_array"],
                    result["timestamp"],
                    raw_text,
                    return_raw_text=return_raw_text,
                )
                result["sentence_info"] = sentence_list
            if "spk_embedding" in result:
                del result["spk_embedding"]

            result["key"] = key
            results_ret_list.append(result)
            end_asr_total = time.time()
            time_escape_total_per_sample = end_asr_total - beg_asr_total
            pbar_total.update(1)
            pbar_total.set_description(
                f"rtf_avg: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
                f"time_speech: {time_speech_total_per_sample: 0.3f}, "
                f"time_escape: {time_escape_total_per_sample:0.3f}"
            )

        return results_ret_list

    def infer_encoder(
        self, input, input_len=None, model=None, kwargs=None, key=None, **cfg
    ):
        kwargs = self.kwargs if kwargs is None else kwargs
        kwargs.update(cfg)
        model = self.model if model is None else model
        model = model.cuda()
        model.eval()

        batch_size = kwargs.get("batch_size", 1)

        key_list, data_list = prepare_data_iterator(
            input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key
        )

        asr_result_list = []
        num_samples = len(data_list)
        for beg_idx in range(0, num_samples, batch_size):
            end_idx = min(num_samples, beg_idx + batch_size)
            data_batch = data_list[beg_idx:end_idx]
            key_batch = key_list[beg_idx:end_idx]
            batch = {"data_in": data_batch, "key": key_batch}
            if (end_idx - beg_idx) == 1 and kwargs.get(
                "data_type", None
            ) == "fbank":  # fbank
                batch["data_in"] = data_batch[0]
                batch["data_lengths"] = input_len

            with torch.no_grad():
                results, meta_data, cache = model.infer_encoder(**batch, **kwargs)
            asr_result_list.extend(results)

        torch.cuda.empty_cache()
        return asr_result_list, cache