505 lines
16 KiB
Python
505 lines
16 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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# Modified from 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker)
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import io
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import os
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import torch
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import requests
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import tempfile
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import contextlib
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import numpy as np
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import librosa as sf
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from typing import Union
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from pathlib import Path
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from typing import Generator, Union
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from abc import ABCMeta, abstractmethod
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import torchaudio.compliance.kaldi as Kaldi
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from funasr.models.transformer.utils.nets_utils import pad_list
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def check_audio_list(audio: list):
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audio_dur = 0
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for i in range(len(audio)):
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seg = audio[i]
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assert seg[1] >= seg[0], "modelscope error: Wrong time stamps."
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assert isinstance(seg[2], np.ndarray), "modelscope error: Wrong data type."
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assert (
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int(seg[1] * 16000) - int(seg[0] * 16000) == seg[2].shape[0]
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), "modelscope error: audio data in list is inconsistent with time length."
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if i > 0:
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assert seg[0] >= audio[i - 1][1], "modelscope error: Wrong time stamps."
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audio_dur += seg[1] - seg[0]
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return audio_dur
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# assert audio_dur > 5, 'modelscope error: The effective audio duration is too short.'
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def sv_preprocess(inputs: Union[np.ndarray, list]):
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output = []
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for i in range(len(inputs)):
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if isinstance(inputs[i], str):
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file_bytes = File.read(inputs[i])
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data, fs = sf.load(io.BytesIO(file_bytes), dtype="float32")
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if len(data.shape) == 2:
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data = data[:, 0]
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data = torch.from_numpy(data).unsqueeze(0)
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data = data.squeeze(0)
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elif isinstance(inputs[i], np.ndarray):
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assert len(inputs[i].shape) == 1, "modelscope error: Input array should be [N, T]"
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data = inputs[i]
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if data.dtype in ["int16", "int32", "int64"]:
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data = (data / (1 << 15)).astype("float32")
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else:
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data = data.astype("float32")
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data = torch.from_numpy(data)
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else:
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raise ValueError(
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"modelscope error: The input type is restricted to audio address and nump array."
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)
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output.append(data)
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return output
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def sv_chunk(vad_segments: list, fs=16000) -> list:
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config = {
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"seg_dur": 1.5,
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"seg_shift": 0.75,
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}
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def seg_chunk(seg_data):
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seg_st = seg_data[0]
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data = seg_data[2]
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chunk_len = int(config["seg_dur"] * fs)
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chunk_shift = int(config["seg_shift"] * fs)
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last_chunk_ed = 0
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seg_res = []
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for chunk_st in range(0, data.shape[0], chunk_shift):
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chunk_ed = min(chunk_st + chunk_len, data.shape[0])
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if chunk_ed <= last_chunk_ed:
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break
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last_chunk_ed = chunk_ed
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chunk_st = max(0, chunk_ed - chunk_len)
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chunk_data = data[chunk_st:chunk_ed]
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if chunk_data.shape[0] < chunk_len:
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chunk_data = np.pad(chunk_data, (0, chunk_len - chunk_data.shape[0]), "constant")
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seg_res.append([chunk_st / fs + seg_st, chunk_ed / fs + seg_st, chunk_data])
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return seg_res
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segs = []
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for i, s in enumerate(vad_segments):
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segs.extend(seg_chunk(s))
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return segs
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def extract_feature(audio):
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features = []
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feature_times = []
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feature_lengths = []
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for au in audio:
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feature = Kaldi.fbank(au.unsqueeze(0), num_mel_bins=80)
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feature = feature - feature.mean(dim=0, keepdim=True)
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features.append(feature)
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feature_times.append(au.shape[0])
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feature_lengths.append(feature.shape[0])
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# padding for batch inference
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features_padded = pad_list(features, pad_value=0)
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# features = torch.cat(features)
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return features_padded, feature_lengths, feature_times
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def postprocess(
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segments: list, vad_segments: list, labels: np.ndarray, embeddings: np.ndarray
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) -> list:
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assert len(segments) == len(labels)
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labels = correct_labels(labels)
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distribute_res = []
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for i in range(len(segments)):
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distribute_res.append([segments[i][0], segments[i][1], labels[i]])
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# merge the same speakers chronologically
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distribute_res = merge_seque(distribute_res)
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# accquire speaker center
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spk_embs = []
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for i in range(labels.max() + 1):
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spk_emb = embeddings[labels == i].mean(0)
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spk_embs.append(spk_emb)
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spk_embs = np.stack(spk_embs)
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def is_overlapped(t1, t2):
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if t1 > t2 + 1e-4:
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return True
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return False
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# distribute the overlap region
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for i in range(1, len(distribute_res)):
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if is_overlapped(distribute_res[i - 1][1], distribute_res[i][0]):
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p = (distribute_res[i][0] + distribute_res[i - 1][1]) / 2
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distribute_res[i][0] = p
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distribute_res[i - 1][1] = p
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# smooth the result
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distribute_res = smooth(distribute_res)
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return distribute_res
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def correct_labels(labels):
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labels_id = 0
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id2id = {}
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new_labels = []
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for i in labels:
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if i not in id2id:
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id2id[i] = labels_id
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labels_id += 1
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new_labels.append(id2id[i])
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return np.array(new_labels)
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def merge_seque(distribute_res):
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res = [distribute_res[0]]
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for i in range(1, len(distribute_res)):
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if distribute_res[i][2] != res[-1][2] or distribute_res[i][0] > res[-1][1]:
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res.append(distribute_res[i])
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else:
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res[-1][1] = distribute_res[i][1]
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return res
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def smooth(res, mindur=1):
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# if only one segment, return directly
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if len(res) < 2:
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return res
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# short segments are assigned to nearest speakers.
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for i in range(len(res)):
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res[i][0] = round(res[i][0], 2)
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res[i][1] = round(res[i][1], 2)
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if res[i][1] - res[i][0] < mindur:
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if i == 0:
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res[i][2] = res[i + 1][2]
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elif i == len(res) - 1:
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res[i][2] = res[i - 1][2]
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elif res[i][0] - res[i - 1][1] <= res[i + 1][0] - res[i][1]:
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res[i][2] = res[i - 1][2]
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else:
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res[i][2] = res[i + 1][2]
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# merge the speakers
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res = merge_seque(res)
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return res
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def distribute_spk(sentence_list, sd_time_list):
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sd_sentence_list = []
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for d in sentence_list:
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sentence_start = d["start"]
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sentence_end = d["end"]
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sentence_spk = 0
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max_overlap = 0
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for sd_time in sd_time_list:
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spk_st, spk_ed, spk = sd_time
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spk_st = spk_st * 1000
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spk_ed = spk_ed * 1000
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overlap = max(min(sentence_end, spk_ed) - max(sentence_start, spk_st), 0)
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if overlap > max_overlap:
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max_overlap = overlap
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sentence_spk = spk
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d["spk"] = int(sentence_spk)
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sd_sentence_list.append(d)
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return sd_sentence_list
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class Storage(metaclass=ABCMeta):
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"""Abstract class of storage.
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All backends need to implement two apis: ``read()`` and ``read_text()``.
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``read()`` reads the file as a byte stream and ``read_text()`` reads
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the file as texts.
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"""
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@abstractmethod
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def read(self, filepath: str):
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pass
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@abstractmethod
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def read_text(self, filepath: str):
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pass
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@abstractmethod
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def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
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pass
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@abstractmethod
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def write_text(self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8") -> None:
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pass
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class LocalStorage(Storage):
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"""Local hard disk storage"""
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def read(self, filepath: Union[str, Path]) -> bytes:
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"""Read data from a given ``filepath`` with 'rb' mode.
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Args:
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filepath (str or Path): Path to read data.
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Returns:
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bytes: Expected bytes object.
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"""
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with open(filepath, "rb") as f:
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content = f.read()
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return content
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def read_text(self, filepath: Union[str, Path], encoding: str = "utf-8") -> str:
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"""Read data from a given ``filepath`` with 'r' mode.
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Args:
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filepath (str or Path): Path to read data.
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encoding (str): The encoding format used to open the ``filepath``.
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Default: 'utf-8'.
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Returns:
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str: Expected text reading from ``filepath``.
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"""
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with open(filepath, "r", encoding=encoding) as f:
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value_buf = f.read()
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return value_buf
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def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
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"""Write data to a given ``filepath`` with 'wb' mode.
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Note:
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``write`` will create a directory if the directory of ``filepath``
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does not exist.
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Args:
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obj (bytes): Data to be written.
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filepath (str or Path): Path to write data.
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"""
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dirname = os.path.dirname(filepath)
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if dirname and not os.path.exists(dirname):
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os.makedirs(dirname, exist_ok=True)
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with open(filepath, "wb") as f:
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f.write(obj)
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def write_text(self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8") -> None:
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"""Write data to a given ``filepath`` with 'w' mode.
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Note:
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``write_text`` will create a directory if the directory of
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``filepath`` does not exist.
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Args:
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obj (str): Data to be written.
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filepath (str or Path): Path to write data.
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encoding (str): The encoding format used to open the ``filepath``.
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Default: 'utf-8'.
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"""
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dirname = os.path.dirname(filepath)
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if dirname and not os.path.exists(dirname):
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os.makedirs(dirname, exist_ok=True)
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with open(filepath, "w", encoding=encoding) as f:
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f.write(obj)
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@contextlib.contextmanager
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def as_local_path(self, filepath: Union[str, Path]) -> Generator[Union[str, Path], None, None]:
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"""Only for unified API and do nothing."""
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yield filepath
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class HTTPStorage(Storage):
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"""HTTP and HTTPS storage."""
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def read(self, url):
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# TODO @wenmeng.zwm add progress bar if file is too large
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r = requests.get(url)
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r.raise_for_status()
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return r.content
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def read_text(self, url):
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r = requests.get(url)
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r.raise_for_status()
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return r.text
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@contextlib.contextmanager
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def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]:
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"""Download a file from ``filepath``.
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``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
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can be called with ``with`` statement, and when exists from the
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``with`` statement, the temporary path will be released.
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Args:
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filepath (str): Download a file from ``filepath``.
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Examples:
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>>> storage = HTTPStorage()
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>>> # After existing from the ``with`` clause,
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>>> # the path will be removed
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>>> with storage.get_local_path('http://path/to/file') as path:
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... # do something here
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"""
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try:
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f = tempfile.NamedTemporaryFile(delete=False)
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f.write(self.read(filepath))
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f.close()
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yield f.name
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finally:
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os.remove(f.name)
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def write(self, obj: bytes, url: Union[str, Path]) -> None:
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raise NotImplementedError("write is not supported by HTTP Storage")
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def write_text(self, obj: str, url: Union[str, Path], encoding: str = "utf-8") -> None:
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raise NotImplementedError("write_text is not supported by HTTP Storage")
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class OSSStorage(Storage):
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"""OSS storage."""
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def __init__(self, oss_config_file=None):
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# read from config file or env var
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raise NotImplementedError("OSSStorage.__init__ to be implemented in the future")
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def read(self, filepath):
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raise NotImplementedError("OSSStorage.read to be implemented in the future")
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def read_text(self, filepath, encoding="utf-8"):
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raise NotImplementedError("OSSStorage.read_text to be implemented in the future")
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@contextlib.contextmanager
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def as_local_path(self, filepath: str) -> Generator[Union[str, Path], None, None]:
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"""Download a file from ``filepath``.
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``as_local_path`` is decorated by :meth:`contextlib.contextmanager`. It
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can be called with ``with`` statement, and when exists from the
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``with`` statement, the temporary path will be released.
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Args:
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filepath (str): Download a file from ``filepath``.
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Examples:
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>>> storage = OSSStorage()
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>>> # After existing from the ``with`` clause,
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>>> # the path will be removed
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>>> with storage.get_local_path('http://path/to/file') as path:
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... # do something here
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"""
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try:
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f = tempfile.NamedTemporaryFile(delete=False)
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f.write(self.read(filepath))
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f.close()
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yield f.name
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finally:
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os.remove(f.name)
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def write(self, obj: bytes, filepath: Union[str, Path]) -> None:
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raise NotImplementedError("OSSStorage.write to be implemented in the future")
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def write_text(self, obj: str, filepath: Union[str, Path], encoding: str = "utf-8") -> None:
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raise NotImplementedError("OSSStorage.write_text to be implemented in the future")
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G_STORAGES = {}
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class File(object):
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_prefix_to_storage: dict = {
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"oss": OSSStorage,
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"http": HTTPStorage,
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"https": HTTPStorage,
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"local": LocalStorage,
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}
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@staticmethod
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def _get_storage(uri):
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assert isinstance(uri, str), f"uri should be str type, but got {type(uri)}"
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if "://" not in uri:
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# local path
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storage_type = "local"
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else:
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prefix, _ = uri.split("://")
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storage_type = prefix
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assert storage_type in File._prefix_to_storage, (
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f"Unsupported uri {uri}, valid prefixs: " f"{list(File._prefix_to_storage.keys())}"
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)
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if storage_type not in G_STORAGES:
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G_STORAGES[storage_type] = File._prefix_to_storage[storage_type]()
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return G_STORAGES[storage_type]
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@staticmethod
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def read(uri: str) -> bytes:
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"""Read data from a given ``filepath`` with 'rb' mode.
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Args:
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filepath (str or Path): Path to read data.
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Returns:
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bytes: Expected bytes object.
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"""
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storage = File._get_storage(uri)
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return storage.read(uri)
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@staticmethod
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def read_text(uri: Union[str, Path], encoding: str = "utf-8") -> str:
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"""Read data from a given ``filepath`` with 'r' mode.
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Args:
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filepath (str or Path): Path to read data.
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encoding (str): The encoding format used to open the ``filepath``.
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Default: 'utf-8'.
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Returns:
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str: Expected text reading from ``filepath``.
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"""
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storage = File._get_storage(uri)
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return storage.read_text(uri)
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@staticmethod
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def write(obj: bytes, uri: Union[str, Path]) -> None:
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"""Write data to a given ``filepath`` with 'wb' mode.
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Note:
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``write`` will create a directory if the directory of ``filepath``
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does not exist.
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Args:
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obj (bytes): Data to be written.
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filepath (str or Path): Path to write data.
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"""
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storage = File._get_storage(uri)
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return storage.write(obj, uri)
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@staticmethod
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def write_text(obj: str, uri: str, encoding: str = "utf-8") -> None:
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"""Write data to a given ``filepath`` with 'w' mode.
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Note:
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``write_text`` will create a directory if the directory of
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``filepath`` does not exist.
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Args:
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obj (str): Data to be written.
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filepath (str or Path): Path to write data.
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encoding (str): The encoding format used to open the ``filepath``.
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Default: 'utf-8'.
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"""
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storage = File._get_storage(uri)
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return storage.write_text(obj, uri)
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@contextlib.contextmanager
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def as_local_path(uri: str) -> Generator[Union[str, Path], None, None]:
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"""Only for unified API and do nothing."""
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storage = File._get_storage(uri)
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with storage.as_local_path(uri) as local_path:
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yield local_path
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