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