From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/utils/wav_utils.py | 158 ++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 156 insertions(+), 2 deletions(-)
diff --git a/funasr/utils/wav_utils.py b/funasr/utils/wav_utils.py
index d8564f2..bd067c2 100644
--- a/funasr/utils/wav_utils.py
+++ b/funasr/utils/wav_utils.py
@@ -2,6 +2,8 @@
import math
import os
+import shutil
+from multiprocessing import Pool
from typing import Any, Dict, Union
import kaldiio
@@ -9,6 +11,7 @@
import numpy as np
import torch
import torchaudio
+import soundfile
import torchaudio.compliance.kaldi as kaldi
@@ -152,7 +155,7 @@
raise TypeError("'dtype' must be a floating point type")
i = np.iinfo(middle_data.dtype)
- abs_max = 2**(i.bits - 1)
+ abs_max = 2 ** (i.bits - 1)
offset = i.min + abs_max
waveform = np.frombuffer(
(middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
@@ -160,7 +163,13 @@
waveform = torch.from_numpy(waveform.reshape(1, -1))
else:
# load pcm from wav, and resample
- waveform, audio_sr = torchaudio.load(wav_file)
+ try:
+ waveform, audio_sr = torchaudio.load(wav_file)
+ except:
+ waveform, audio_sr = soundfile.read(wav_file, dtype='float32')
+ if waveform.ndim == 2:
+ waveform = waveform[:, 0]
+ waveform = torch.tensor(np.expand_dims(waveform, axis=0))
waveform = waveform * (1 << 15)
waveform = torch_resample(waveform, audio_sr, model_sr)
@@ -176,3 +185,148 @@
input_feats = mat
return input_feats
+
+
+def wav2num_frame(wav_path, frontend_conf):
+ try:
+ waveform, sampling_rate = torchaudio.load(wav_path)
+ except:
+ waveform, sampling_rate = soundfile.read(wav_path)
+ waveform = torch.tensor(np.expand_dims(waveform, axis=0))
+ speech_length = (waveform.shape[1] / sampling_rate) * 1000.
+ n_frames = (waveform.shape[1] * 1000.0) / (sampling_rate * frontend_conf["frame_shift"] * frontend_conf["lfr_n"])
+ feature_dim = frontend_conf["n_mels"] * frontend_conf["lfr_m"]
+ return n_frames, feature_dim, speech_length
+
+
+def calc_shape_core(root_path, frontend_conf, speech_length_min, speech_length_max, idx):
+ wav_scp_file = os.path.join(root_path, "wav.scp.{}".format(idx))
+ shape_file = os.path.join(root_path, "speech_shape.{}".format(idx))
+ with open(wav_scp_file) as f:
+ lines = f.readlines()
+ with open(shape_file, "w") as f:
+ for line in lines:
+ sample_name, wav_path = line.strip().split()
+ n_frames, feature_dim, speech_length = wav2num_frame(wav_path, frontend_conf)
+ write_flag = True
+ if speech_length_min > 0 and speech_length < speech_length_min:
+ write_flag = False
+ if speech_length_max > 0 and speech_length > speech_length_max:
+ write_flag = False
+ if write_flag:
+ f.write("{} {},{}\n".format(sample_name, str(int(np.ceil(n_frames))), str(int(feature_dim))))
+ f.flush()
+
+
+def calc_shape(data_dir, dataset, frontend_conf, speech_length_min=-1, speech_length_max=-1, nj=32):
+ shape_path = os.path.join(data_dir, dataset, "shape_files")
+ if os.path.exists(shape_path):
+ assert os.path.exists(os.path.join(data_dir, dataset, "speech_shape"))
+ print('Shape file for small dataset already exists.')
+ return
+ os.makedirs(shape_path, exist_ok=True)
+
+ # split
+ wav_scp_file = os.path.join(data_dir, dataset, "wav.scp")
+ with open(wav_scp_file) as f:
+ lines = f.readlines()
+ num_lines = len(lines)
+ num_job_lines = num_lines // nj
+ start = 0
+ for i in range(nj):
+ end = start + num_job_lines
+ file = os.path.join(shape_path, "wav.scp.{}".format(str(i + 1)))
+ with open(file, "w") as f:
+ if i == nj - 1:
+ f.writelines(lines[start:])
+ else:
+ f.writelines(lines[start:end])
+ start = end
+
+ p = Pool(nj)
+ for i in range(nj):
+ p.apply_async(calc_shape_core,
+ args=(shape_path, frontend_conf, speech_length_min, speech_length_max, str(i + 1)))
+ print('Generating shape files, please wait a few minutes...')
+ p.close()
+ p.join()
+
+ # combine
+ file = os.path.join(data_dir, dataset, "speech_shape")
+ with open(file, "w") as f:
+ for i in range(nj):
+ job_file = os.path.join(shape_path, "speech_shape.{}".format(str(i + 1)))
+ with open(job_file) as job_f:
+ lines = job_f.readlines()
+ f.writelines(lines)
+ print('Generating shape files done.')
+
+
+def generate_data_list(data_dir, dataset, nj=100):
+ split_dir = os.path.join(data_dir, dataset, "split")
+ if os.path.exists(split_dir):
+ assert os.path.exists(os.path.join(data_dir, dataset, "data.list"))
+ print('Data list for large dataset already exists.')
+ return
+ os.makedirs(split_dir, exist_ok=True)
+
+ with open(os.path.join(data_dir, dataset, "wav.scp")) as f_wav:
+ wav_lines = f_wav.readlines()
+ with open(os.path.join(data_dir, dataset, "text")) as f_text:
+ text_lines = f_text.readlines()
+ total_num_lines = len(wav_lines)
+ num_lines = total_num_lines // nj
+ start_num = 0
+ for i in range(nj):
+ end_num = start_num + num_lines
+ split_dir_nj = os.path.join(split_dir, str(i + 1))
+ os.mkdir(split_dir_nj)
+ wav_file = os.path.join(split_dir_nj, 'wav.scp')
+ text_file = os.path.join(split_dir_nj, "text")
+ with open(wav_file, "w") as fw, open(text_file, "w") as ft:
+ if i == nj - 1:
+ fw.writelines(wav_lines[start_num:])
+ ft.writelines(text_lines[start_num:])
+ else:
+ fw.writelines(wav_lines[start_num:end_num])
+ ft.writelines(text_lines[start_num:end_num])
+ start_num = end_num
+
+ data_list_file = os.path.join(data_dir, dataset, "data.list")
+ with open(data_list_file, "w") as f_data:
+ for i in range(nj):
+ wav_path = os.path.join(split_dir, str(i + 1), "wav.scp")
+ text_path = os.path.join(split_dir, str(i + 1), "text")
+ f_data.write(wav_path + " " + text_path + "\n")
+
+def filter_wav_text(data_dir, dataset):
+ wav_file = os.path.join(data_dir,dataset,"wav.scp")
+ text_file = os.path.join(data_dir, dataset, "text")
+ with open(wav_file) as f_wav, open(text_file) as f_text:
+ wav_lines = f_wav.readlines()
+ text_lines = f_text.readlines()
+ os.rename(wav_file, "{}.bak".format(wav_file))
+ os.rename(text_file, "{}.bak".format(text_file))
+ wav_dict = {}
+ for line in wav_lines:
+ parts = line.strip().split()
+ if len(parts) != 2:
+ continue
+ sample_name, wav_path = parts
+ wav_dict[sample_name] = wav_path
+ text_dict = {}
+ for line in text_lines:
+ parts = line.strip().split()
+ if len(parts) < 2:
+ continue
+ sample_name = parts[0]
+ text_dict[sample_name] = " ".join(parts[1:]).lower()
+ filter_count = 0
+ with open(wav_file, "w") as f_wav, open(text_file, "w") as f_text:
+ for sample_name, wav_path in wav_dict.items():
+ if sample_name in text_dict.keys():
+ f_wav.write(sample_name + " " + wav_path + "\n")
+ f_text.write(sample_name + " " + text_dict[sample_name] + "\n")
+ else:
+ filter_count += 1
+ print("{}/{} samples in {} are filtered because of the mismatch between wav.scp and text".format(len(wav_lines), filter_count, dataset))
--
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