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