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jblitzar
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github-python

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lon, lat, i1, i2, j1, j2 = eddy.restrict_lonlat(lon, lat, lon1, lon2, lat1, lat2)
# Loop over time
lon_eddies_a = []
lat_eddies_a = []
amp_eddies_a = []
area_eddies_a = []
scale_eddies_a = []
lon_eddies_c = []
lat_eddies_c = []
amp_eddies_c = []
area_eddies_c = []
scale_eddies_c = []
print 'eddy detection started'
print "number of time steps to loop over: ",T
for tt in range(T):
print "timestep: ",tt+1,". out of: ", T
# Load map of sea surface height (SSH)
eta, eta_miss = eddy.load_eta(run, tt, i1, i2, j1, j2)
eta = eddy.remove_missing(eta, missing=eta_miss, replacement=np.nan)
#eddy.quick_plot(eta,findrange=True)
#
## Spatially filter SSH field
#
eta_filt = eddy.spatial_filter(eta, lon, lat, res, cut_lon, cut_lat)
#eddy.quick_plot(eta_filt,findrange=True)
#
## Detect lon and lat coordinates of eddies
#
lon_eddies, lat_eddies, amp, area, scale = eddy.detect_eddies(eta_filt, lon, lat, ssh_crits, res, Npix_min, Npix_max, amp_thresh, d_thresh, cyc='anticyclonic')
lon_eddies_a.append(lon_eddies)
lat_eddies_a.append(lat_eddies)
amp_eddies_a.append(amp)
area_eddies_a.append(area)
scale_eddies_a.append(scale)
lon_eddies, lat_eddies, amp, area, scale = eddy.detect_eddies(eta_filt, lon, lat, ssh_crits, res, Npix_min, Npix_max, amp_thresh, d_thresh, cyc='cyclonic')
lon_eddies_c.append(lon_eddies)
lat_eddies_c.append(lat_eddies)
amp_eddies_c.append(amp)
area_eddies_c.append(area)
scale_eddies_c.append(scale)
# Plot map of filtered SSH field
eddies_a=(lon_eddies_a[tt],lat_eddies_a[tt])
eddies_c=(lon_eddies_c[tt],lat_eddies_c[tt])
eddy.detection_plot(tt,lon,lat,eta,eta_filt,eddies_a,eddies_c,'rawtoo',plot_dir,findrange=False)
# Combine eddy information from all days into a list
eddies = eddy.eddies_list(lon_eddies_a, lat_eddies_a, amp_eddies_a, area_eddies_a, scale_eddies_a, lon_eddies_c, lat_eddies_c, amp_eddies_c, area_eddies_c, scale_eddies_c)
np.savez(data_dir+'eddy_det_'+run, eddies=eddies)
# <FILESEP>
import argparse
import glob
import os
import sys
import jaconv
from faster_whisper import WhisperModel
from tqdm import tqdm
def load_whisper_model(model_size: str = "large-v2"):
print("Whisperモデルをロード中...")
model = WhisperModel(model_size, device="cuda", compute_type="float16")
print("Whisperモデルをロードしました。")
return model
def transcribe(
model: WhisperModel, audio_path: str, initial_prompt: str, allow_multi_segment=True
):
# print(f"{audio_path}を処理中...")
segments, _ = model.transcribe(
audio_path, beam_size=5, language="ja", initial_prompt=initial_prompt
)
texts = [segment.text for segment in segments]
if len(texts) == 0:
return None
elif len(texts) > 1:
# print("セグメントが複数あります：")
# print(texts)
if allow_multi_segment:
result = "".join(texts)
else:
# print("セグメントが複数あるので、このファイルは無視します。")
return None
else:
result = texts[0]
result = jaconv.normalize(result)