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prepare_data.py
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"""
Author
* Cem Subakan 2020
* Will Ravenscroft 2021
The .csv preperation functions for WHAMR
"""
import os, glob, csv, json
import soundfile as sf
set_map = {"tr":"train","cv":"valid","tt":"test"}
def get_rir_paths(rir_path, sets=set_map.keys()):
rir_directory = {
set :
{
"s1" :
{
os.path.basename(filepath).replace("0_0_","") : filepath
for filepath in glob.glob(os.path.join(rir_path,set,"0_0_*"))
},
"s2" :
{
os.path.basename(filepath).replace("0_1_","") : filepath
for filepath in glob.glob(os.path.join(rir_path,set,"0_1_*"))
}
}
for set in sets
}
return rir_directory
def get_meta_data(creation_path, wham_noise_path):
"""
creation_path = Path to data folder in WHAMR! creation scripts
"""
rir_csv_paths = {
os.path.basename(fname).replace("reverb_params_","").replace(".csv","") : fname
for fname in glob.glob(os.path.join(creation_path,"data","reverb_params*.csv"))
}
# mix_csv_paths = {
# fname.replace("reverb_params_","").replace(".csv") : fname
# for fname in glob.glob(os.path.join(creation_path,"data","reverb_params*.csv"))
# }
noise_csv_paths = {
os.path.basename(fname).replace("mix_param_meta_","").replace(".csv","") : fname
for fname in glob.glob(os.path.join(wham_noise_path,"metadata","mix_param_meta*.csv"))
}
sets = ["cv","tr","tt"]
directory = {}
for i, set in enumerate(sets):
try:
with open(rir_csv_paths[set],'r') as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader):
room_size = float(row['room_x'])*float(row['room_y'])*float(row['room_z'])
directory[row["utterance_id"]]={
"t60":row["T60"],
"room_size":room_size,
"room_x":row["room_x"],
"room_y":row["room_y"],
"room_z":row["room_z"],
"micL_x":row["micL_x"],
"micL_y":row["micL_y"],
"micR_x":row["micR_x"],
"micR_y":row["micR_y"],
"mic_z":row["mic_z"],
"s1_x":row["s1_x"],
"s1_y":row["s1_y"],
"s1_z":row["s1_z"],
"s2_x":row["s2_x"],
"s2_y":row["s2_y"],
"s2_z":row["s2_z"]
}
with open(noise_csv_paths[set],'r') as f:
reader = csv.DictReader(f)
for i, row in enumerate (reader):
directory[row["utterance_id"]]["snr"]=float(row["target_speaker1_snr_db"])
except KeyError as e:
rir_key = "Keys found (RIRs):"+str(rir_csv_paths.keys())
noise_key = "Keys found (noises):"+str(noise_csv_paths.keys())
raise KeyError(str(e)+'. '+rir_key+'. '+noise_key)
return directory
def get_transcriptions(
wsj0_path,
case='u',
filters=["\.PERIOD","\.COMMA","\-HYPHEN","\-\-DASH","\\\"DOUBLE\-QUOTE"]
): #/path/to/...11.-1.1 etc.
dot_files = (glob.glob(os.path.join(wsj0_path,"*/*/*/*.dot"))+
glob.glob(os.path.join(wsj0_path,"*/*/*/*/*.dot"))+
glob.glob(os.path.join(wsj0_path,"*/*/*/*/*/*.dot"))+
glob.glob(os.path.join(wsj0_path,"*/*/*/*/*/*/*.dot"))+
glob.glob(os.path.join(wsj0_path,"*/*/*/*/*/*/*/*.dot")))
test_file = "/share/mini1/data/audvis/pub/asr/studio/us/wsj/v2/wsj0/11-10.1/wsj0/transcrp/dots/si_tr_s/01v/01vo0300.dot"
test_label = "01vo030q"
assert test_file in dot_files
dot_directory = {}
for dot in dot_files:
with open(dot,'r') as f:
for line in f:
try:
line = line.replace(")","").replace("(","")
utterance, label = line[:-9], line[-9:]
except ValueError as e:
raise ValueError("Can't split line \""+line+"\"")
label = label.replace(")","").replace("\n","")
dot_directory[label] = utterance.upper() if 'u' else utterance.lower()
return dot_directory
def prepare_wham_whamr_csv(
datapath,
savepath,
skip_prep=False,
fs=8000,
mini=False,
mix_folder="mix_both_reverb",
target_condition="anechoic",
set_types=["tr", "cv", "tt"],
num_spks=2,
alternate_path=None,
creation_path=None,
wham_noise_path=None,
version="min",
wsj0_path=None, # for transcriptions
eval_original=False,
meta_dump=False,
use_rirs=False,
rir_path=None,
extended=False,
savename="whamr_"
):
"""
Prepares the csv files for wham or whamr dataset
Arguments:
----------
datapath (str) : path for the wsj0-mix dataset.
savepath (str) : path where we save the csv file.
skip_prep (bool): If True, skip data preparation
"""
if skip_prep:
return
if "whamr" in datapath:
# if we want to train a model on the whamr dataset
create_wham_whamr_csv(datapath, savepath, fs,mix_folder=mix_folder,
alternate_path=alternate_path, num_spks=num_spks, target_condition=target_condition,
version=version, creation_path=creation_path, wham_noise_path=wham_noise_path,
wsj0_path=wsj0_path, eval_original=eval_original, meta_dump=meta_dump, rir_path=rir_path,
use_rirs=use_rirs,set_types=set_types,extended=extended,savename=savename
)
elif "wham" in datapath:
# if we want to train a model on the original wham dataset
create_wham_whamr_csv(
datapath, savepath, fs, savename="whamorg_", add_reverb=False, mini=mini,
mix_folder=mix_folder.replace("reverb","").replace("anechoic","")
)
else:
raise ValueError("Unsupported Dataset at: "+datapath)
def create_wham_whamr_csv(
datapath,
savepath,
fs,
version="min",
mix_folder="mix_both_reverb",
target_condition="anechoic",
savename="whamr_",
set_types=["tr", "cv", "tt"],
add_reverb=True,
mini=False,
num_spks=2,
alternate_path=None,
creation_path=None,
wham_noise_path=None,
wsj0_path=None, # for transcriptions
eval_original=False,
meta_dump = False, # grabs every kind of meta data for room simulation
use_rirs = False,
rir_path=None,
extended=False
):
"""
This function creates the csv files to get the speechbrain data loaders for the whamr dataset.
Arguments:
datapath (str) : path for the wsj0-mix dataset.
savepath (str) : path where we save the csv file
fs (int) : the sampling rate
version (str) : min or max
savename (str) : the prefix to use for the .csv files
set_types (list) : the sets to create
"""
if alternate_path==None:
target_path = datapath
else:
target_path = alternate_path
if creation_path != None and wham_noise_path != None:
extra = True
directory = get_meta_data(creation_path, wham_noise_path)
else:
extra = False
if not wsj0_path == None:
transcription_directory = get_transcriptions(wsj0_path)
transcribe = True
else:
transcribe = False
# if not rir_path == None:
# rir_directory= get_rir_paths(rir_path)
# use_rirs = True
# else:
# use_rirs = False
if fs == 8000:
sample_rate = "8k"
elif fs == 16000:
sample_rate = "16k"
else:
raise ValueError("Unsupported sampling rate")
if add_reverb:
mix = mix_folder+"/"
s1 = "s1_"+target_condition+"/"
s2 = "s2_"+target_condition+"/"
else:
mix = mix_folder+"/"
s1 = "s1/"
s2 = "s2/"
if eval_original:
s1_eval = "s1_anechoic/"
s2_eval = "s2_anechoic/"
for set_type in set_types:
mix_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, mix,
)
if eval_original and (set_type == "tt" or set_type == "cv"):
s1_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s1_eval,
)
if num_spks==2:
s2_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s2_eval,
)
else:
s1_path = os.path.join(
target_path, "wav{}".format(sample_rate), version, set_type, s1,
)
if num_spks==2:
s2_path = os.path.join(
target_path, "wav{}".format(sample_rate), version, set_type, s2,
)
noise_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, "noise/"
)
# rir_path = os.path.join(
# datapath, "wav{}".format(sample_rate), version, set_type, "rirs/"
# )
files = os.listdir(mix_path)
mix_fl_paths = [mix_path + fl for fl in files]
s1_fl_paths = [s1_path + fl for fl in files]
if num_spks==2:
s2_fl_paths = [s2_path + fl for fl in files]
noise_fl_paths = [noise_path + fl for fl in files]
# rir_fl_paths = [rir_path + fl + ".t" for fl in files]
# if not rir_path ==None:
# rir_paths = rir_directory[set_type]
if num_spks==1:
csv_columns = [
"ID",
"duration",
"mix_wav",
"mix_wav_format",
"mix_wav_opts",
"s1_wav",
"s1_wav_format",
"s1_wav_opts",
"noise_wav",
"noise_wav_format",
"noise_wav_opts",
# "rir_t",
# "rir_format",
# "rir_opts",
"t60",
"room_size",
"snr",
"s1_dot"
]
else:
csv_columns = [
"ID",
"duration",
"mix_wav",
"mix_wav_format",
"mix_wav_opts",
"s1_wav",
"s1_wav_format",
"s1_wav_opts",
"s2_wav",
"s2_wav_format",
"s2_wav_opts",
"noise_wav",
"noise_wav_format",
"noise_wav_opts",
# "rir_t",
# "rir_format",
# "rir_opts",
"t60",
"room_size",
"snr",
"s1_dot",
"s2_dot"
]
if meta_dump:
extra_cols = [
"room_x",
"room_y",
"room_z",
"micL_x",
"micL_y",
"micR_x",
"micR_y",
"mic_z",
"s1_x",
"s1_y",
"s1_z",
"s2_x",
"s2_y",
"s2_z"
]
csv_columns = csv_columns + extra_cols
if use_rirs:
extra_cols = [
"s1_rir",
"s2_rir"
]
csv_columns = csv_columns + extra_cols
with open(
(
os.path.join(savepath, savename + set_type + ".csv") if not extended else os.path.join(savepath, savename + set_type + "_ext.csv")
), "w"
) as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
if mini and (num_spks==1):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
elif (not mini) and (num_spks==1):
zipped = zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
elif mini and (num_spks==2):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
else:
zipped = zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
for (i, packed,) in enumerate(zipped):
if num_spks==1:
mix_path, s1_path, noise_path = packed
basename = os.path.basename(mix_path)
if transcribe:
s1_basename = os.path.basename(s1_path).replace(".wav","")
s1_dot = transcription_directory[s1_basename]
else:
s1_dot=None
row = {
"ID": i,
"duration": 1.0,
"mix_wav": mix_path,
"mix_wav_format": "wav",
"mix_wav_opts": None,
"s1_wav": s1_path,
"s1_wav_format": "wav",
"s1_wav_opts": None,
"noise_wav": noise_path,
"noise_wav_format": "wav",
"noise_wav_opts": None,
# "rir_t": rir_path,
# "rir_format": ".t",
# "rir_opts": None,
"t60": directory[basename]["t60"] if extra else None,
"room_size": directory[basename]["room_size"] if extra else None,
"snr": directory[basename]["snr"] if extra else None,
"s1_dot": s1_dot
}
else:
mix_path, s1_path, s2_path, noise_path = packed
basename = os.path.basename(mix_path)
if transcribe:
s1_basename = os.path.basename(s1_path).replace(".wav","")
s1_dot = transcription_directory[s1_basename]
s2_basename = os.path.basename(s2_path).replace(".wav","")
s2_dot = transcription_directory[s2_basename]
else:
s1_dot=None
s2_dot=None
row = {
"ID": i,
"duration": 1.0,
"mix_wav": mix_path,
"mix_wav_format": "wav",
"mix_wav_opts": None,
"s1_wav": s1_path,
"s1_wav_format": "wav",
"s1_wav_opts": None,
"s2_wav": s2_path,
"s2_wav_format": "wav",
"s2_wav_opts": None,
"noise_wav": noise_path,
"noise_wav_format": "wav",
"noise_wav_opts": None,
# "rir_t": rir_path,
# "rir_format": ".t",
# "rir_opts": None,
"t60": directory[basename]["t60"] if extra else None,
"room_size": directory[basename]["room_size"] if extra else None,
"snr": directory[basename]["snr"] if extra else None,
"s1_dot": s1_dot,
"s2_dot": s2_dot,
}
if meta_dump:
row["room_x"] = directory[basename]["room_x"]
row["room_y"] = directory[basename]["room_y"]
row["room_z"] = directory[basename]["room_z"]
row["micL_x"] = directory[basename]["micL_x"]
row["micL_y"] = directory[basename]["micL_y"]
row["micR_x"] = directory[basename]["micR_x"]
row["micR_y"] = directory[basename]["micR_y"]
row["mic_z"] = directory[basename]["mic_z"]
row["s1_x"] = directory[basename]["mic_z"]
row["s1_y"] = directory[basename]["s1_y"]
row["s1_z"] = directory[basename]["s1_z"]
row["s2_x"] = directory[basename]["s2_x"]
row["s2_y"] = directory[basename]["s2_y"]
row["s2_z"] = directory[basename]["s2_z"]
if use_rirs:
# row["s1_rir"] = rir_paths["s1"][basename]
# row["s2_rir"] = rir_paths["s2"][basename]
row["s1_rir"] = s1_path.replace("s1_anechoic","s1_rir")
if num_spks == 2:
row["s2_rir"] = s2_path.replace("s2_anechoic","s2_rir")
writer.writerow(row)
def create_whamr_rir_csv(datapath, savepath):
"""
This function creates the csv files to get the data loaders for the whamr dataset.
Arguments:
datapath (str) : path for the whamr rirs.
savepath (str) : path where we save the csv file
"""
csv_columns = ["ID", "duration", "wav", "wav_format", "wav_opts"]
files = os.listdir(datapath)
all_paths = [os.path.join(datapath, fl) for fl in files]
with open(savepath + "/whamr_rirs.csv", "w") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
writer.writeheader()
for i, wav_path in enumerate(all_paths):
row = {
"ID": i,
"duration": 2.0,
"wav": wav_path,
"wav_format": "wav",
"wav_opts": None,
}
writer.writerow(row)
def create_wham_whamr_json(
datapath,
savepath,
fs,
version="min",
mix_folder="mix_both_reverb",
target_condition="anechoic",
savename="whamr_",
set_types=["tr", "cv", "tt"],
add_reverb=True,
mini=False,
num_spks=2,
alternate_path=None,
creation_path=None,
wham_noise_path=None,
wsj0_path=None, # for transcriptions
rir_path=None,
eval_original=False,
):
"""
This function creates the csv files to get the speechbrain data loaders for the whamr dataset.
Arguments:
datapath (str) : path for the wsj0-mix dataset.
savepath (str) : path where we save the csv file
fs (int) : the sampling rate
version (str) : min or max
savename (str) : the prefix to use for the .csv files
set_types (list) : the sets to create
"""
if alternate_path==None:
target_path = datapath
else:
target_path = alternate_path
if creation_path != None and wham_noise_path != None:
extra = True
directory = get_meta_data(creation_path, wham_noise_path)
else:
extra = False
if not wsj0_path == None:
transcription_directory = get_transcriptions(wsj0_path)
transcribe = True
else:
transcribe = False
if fs == 8000:
sample_rate = "8k"
elif fs == 16000:
sample_rate = "16k"
else:
raise ValueError("Unsupported sampling rate")
if add_reverb:
mix = mix_folder+"/"
s1 = "s1_"+target_condition+"/"
s2 = "s2_"+target_condition+"/"
else:
mix = mix_folder+"/"
s1 = "s1/"
s2 = "s2/"
if eval_original:
s1_eval = "s1_anechoic/"
s2_eval = "s2_anechoic/"
for set_type in set_types:
mix_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, mix,
)
if eval_original and (set_type == "tt" or set_type == "cv"):
s1_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s1_eval,
)
if num_spks==2:
s2_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s2_eval,
)
else:
s1_path = os.path.join(
target_path, "wav{}".format(sample_rate), version, set_type, s1,
)
if num_spks==2:
s2_path = os.path.join(
target_path, "wav{}".format(sample_rate), version, set_type, s2,
)
noise_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, "noise/"
)
# rir_path = os.path.join(
# datapath, "wav{}".format(sample_rate), version, set_type, "rirs/"
# )
files = os.listdir(mix_path)
mix_fl_paths = [mix_path + fl for fl in files]
s1_fl_paths = [s1_path + fl for fl in files]
if num_spks==2:
s2_fl_paths = [s2_path + fl for fl in files]
noise_fl_paths = [noise_path + fl for fl in files]
# rir_fl_paths = [rir_path + fl + ".t" for fl in files]
if num_spks==1:
csv_columns = [
"ID",
"duration",
"mix_wav",
"mix_wav_format",
"mix_wav_opts",
"s1_wav",
"s1_wav_format",
"s1_wav_opts",
"noise_wav",
"noise_wav_format",
"noise_wav_opts",
# "rir_t",
# "rir_format",
# "rir_opts",
"t60",
"room_size",
"snr",
"s1_dot"
]
else:
csv_columns = [
"ID",
"duration",
"mix_wav",
"mix_wav_format",
"mix_wav_opts",
"s1_wav",
"s1_wav_format",
"s1_wav_opts",
"s2_wav",
"s2_wav_format",
"s2_wav_opts",
"noise_wav",
"noise_wav_format",
"noise_wav_opts",
# "rir_t",
# "rir_format",
# "rir_opts",
"t60",
"room_size",
"snr",
"s1_dot",
"s2_dot"
]
with open(
os.path.join(savepath, savename + set_type + ".csv"), "w"
) as csvfile:
json_dict = {}
if mini and (num_spks==1):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
elif (not mini) and (num_spks==1):
zipped = zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
elif mini and (num_spks==2):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
else:
zipped = zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
for (i, packed,) in enumerate(zipped):
if num_spks==1:
mix_path, s1_path, noise_path = packed
basename = os.path.basename(mix_path)
data, fs = sf.read(mix_path)
duration = len(data)/fs
if transcribe:
s1_basename = os.path.basename(s1_path).replace(".wav","")
s1_dot = transcription_directory[s1_basename]
else:
s1_dot=None
row = {
"ID": i,
"duration": duration,
"mix_wav": mix_path,
"mix_wav_format": "wav",
"mix_wav_opts": None,
"s1_wav": s1_path,
"s1_wav_format": "wav",
"s1_wav_opts": None,
"noise_wav": noise_path,
"noise_wav_format": "wav",
"noise_wav_opts": None,
# "rir_t": rir_path,
# "rir_format": ".t",
# "rir_opts": None,
"t60": directory[basename]["t60"] if extra else None,
"room_size": directory[basename]["room_size"] if extra else None,
"snr": directory[basename]["snr"] if extra else None,
"s1_dot": s1_dot
}
else:
mix_path, s1_path, s2_path, noise_path = packed
basename = os.path.basename(mix_path)
data, fs = sf.read(mix_path)
duration = len(data)/fs
if transcribe:
s1_basename = os.path.basename(s1_path).replace(".wav","")
s1_dot = transcription_directory[s1_basename]
s2_basename = os.path.basename(s2_path).replace(".wav","")
s2_dot = transcription_directory[s2_basename]
else:
s1_dot=None
s2_dot=None
row = {
"ID": i,
"duration": duration,
"mix_wav": mix_path,
"mix_wav_format": "wav",
"mix_wav_opts": None,
"s1_wav": s1_path,
"s1_wav_format": "wav",
"s1_wav_opts": None,
"s2_wav": s2_path,
"s2_wav_format": "wav",
"s2_wav_opts": None,
"noise_wav": noise_path,
"noise_wav_format": "wav",
"noise_wav_opts": None,
# "rir_t": rir_path,
# "rir_format": ".t",
# "rir_opts": None,
"t60": directory[basename]["t60"] if extra else None,
"room_size": directory[basename]["room_size"] if extra else None,
"snr": directory[basename]["snr"] if extra else None,
"s1_dot": s1_dot,
"s2_dot": s2_dot,
}
json_dict[basename.replace(".wav","")+"_#_"+target_condition] = row
json_fname = os.path.join(savepath,set_type+".json")
with open(json_fname, 'w') as f:
json_string= json.dumps(json_dict, sort_keys=False, indent=4)
f.write(json_string)
def tokenizer_data_prep(
datapath,
savepath,
fs,
version="min",
mix_folder="mix_both_reverb",
target_condition="anechoic",
savename="whamr_",
set_types=["tr", "cv", "tt"],
add_reverb=True,
mini=False,
num_spks=2,
wsj0_path=None, # for transcriptions
):
"""
This function creates the csv files to get the speechbrain data loaders for the whamr dataset.
Arguments:
datapath (str) : path for the wsj0-mix dataset.
savepath (str) : path where we save the csv file
fs (int) : the sampling rate
version (str) : min or max
savename (str) : the prefix to use for the .csv files
set_types (list) : the sets to create
"""
os.makedirs(savepath, exist_ok=True)
if not wsj0_path == None:
transcription_directory = get_transcriptions(wsj0_path)
transcribe = True
else:
transcribe = False
if fs == 8000:
sample_rate = "8k"
elif fs == 16000:
sample_rate = "16k"
else:
raise ValueError("Unsupported sampling rate")
if add_reverb:
mix = mix_folder+"/"
s1 = "s1_"+target_condition+"/"
s2 = "s2_"+target_condition+"/"
else:
mix = mix_folder+"/"
s1 = "s1/"
s2 = "s2/"
for set_type in set_types:
mix_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, mix,
)
s1_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s1,
)
if num_spks==2:
s2_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, s2,
)
noise_path = os.path.join(
datapath, "wav{}".format(sample_rate), version, set_type, "noise/"
)
# rir_path = os.path.join(
# datapath, "wav{}".format(sample_rate), version, set_type, "rirs/"
# )
files = os.listdir(mix_path)
mix_fl_paths = [mix_path + fl for fl in files]
s1_fl_paths = [s1_path + fl for fl in files]
if num_spks==2:
s2_fl_paths = [s2_path + fl for fl in files]
noise_fl_paths = [noise_path + fl for fl in files]
# rir_fl_paths = [rir_path + fl + ".t" for fl in files]
with open(
os.path.join(savepath, set_map[set_type] + ".json"), "w"
) as jsonfile:
json_dict = {}
if mini and (num_spks==1):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
elif (not mini) and (num_spks==1):
zipped = zip(
mix_fl_paths,
s1_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
elif mini and (num_spks==2):
zipped = list(zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
))[:len(mix_fl_paths)//4]
else:
zipped = zip(
mix_fl_paths,
s1_fl_paths,
s2_fl_paths,
noise_fl_paths,
# rir_fl_paths,
)
for (i, packed,) in enumerate(zipped):
if num_spks==1:
mix_path, s1_path, noise_path = packed
basename = os.path.basename(mix_path)
data, fs = sf.read(mix_path)
duration = len(data)/fs
if transcribe:
s1_basename = os.path.basename(s1_path).replace(".wav","")
s1_dot = transcription_directory[s1_basename]
else:
s1_dot=None
json_dict[basename.replace(".wav","")+"#s1"] = {
"length": duration,
"wav": s1_path,
"words": s1_dot
}
else:
mix_path, s1_path, s2_path, noise_path = packed
basename = os.path.basename(mix_path)
data, fs = sf.read(mix_path)
duration = len(data)/fs
try:
if transcribe:
s1_key, _, s2_key, _ = basename.replace(".wav","").split("_")
s1_dot = transcription_directory[s1_key]
s2_dot = transcription_directory[s2_key]
else:
s1_dot=None
s2_dot=None
except KeyError as e:
err_str = "Error with key "+str(e)+". Available keys: "+str(list(transcription_directory.keys())[:20])+"..."
raise KeyError(err_str)
json_dict[basename.replace(".wav","")+"#s1"] = {
"length": duration,
"wav": s1_path,
"words": s1_dot
}
json_dict[basename.replace(".wav","")+"#s2"] = {
"length": duration,
"wav": s2_path,
"words": s2_dot
}
json_string= json.dumps(json_dict, sort_keys=False, indent=4)
try:
jsonfile.write(json_string)
except ValueError as e:
raise ValueError("Error writing json dump "+json_string)
def prepare_multi_whamr_csv(
datapath,
savepath,
skip_prep=False,
fs=8000,
mini=False,
mix_folder="mix_both_reverb",
target_folders=["s1_anechoic","s2_anechoic"],
set_types=["tr", "cv", "tt"],
num_spks=2,
alternate_path=None,
creation_path=None,
wham_noise_path=None,
version="min",
wsj0_path=None, # for transcriptions
eval_original=False,
meta_dump=False,
use_rirs=False,
rir_path=None,
extended=False,
savename="whamr_"
):
"""
Prepares the csv files for wham or whamr dataset
Arguments:
----------
datapath (str) : path for the wsj0-mix dataset.
savepath (str) : path where we save the csv file.
skip_prep (bool): If True, skip data preparation
"""
if skip_prep:
return
if "whamr" in datapath:
# if we want to train a model on the whamr dataset
create_multi_whamr_csv(datapath, savepath, fs,mix_folder=mix_folder,
alternate_path=alternate_path, num_spks=num_spks, target_folders=target_folders,
version=version, creation_path=creation_path, wham_noise_path=wham_noise_path,
wsj0_path=wsj0_path, eval_original=eval_original, meta_dump=meta_dump, rir_path=rir_path,
use_rirs=use_rirs,set_types=set_types,extended=extended,savename=savename
)
elif "wham" in datapath:
# if we want to train a model on the original wham dataset
create_wham_whamr_csv(
datapath, savepath, fs, savename="whamorg_", add_reverb=False, mini=mini,
mix_folder=mix_folder.replace("reverb","").replace("anechoic","")
)
else:
raise ValueError("Unsupported Dataset at: "+datapath)
def create_multi_whamr_csv(
datapath,
savepath,