3 files changed, 32 insertions, 37 deletions
diff --git a/animism-align/README.md b/animism-align/README.md
index ac00d94..eb12c30 100644
--- a/animism-align/README.md
+++ b/animism-align/README.md
@@ -11,13 +11,12 @@ conda create env -f environment.yml
 npm install
 ```
 
-## Generating waveform peaks
+## Proper encoding of an MP3
 
-Make sure your source sound is encoded as a 192 kbps CBR MP3 (for accurate time sync), then generate the peaks:
+Make sure your source sound is encoded as a 192 kbps CBR MP3 (for accurate time sync when using the editor).
 
 ```
 ffmpeg -i original.wav -ar 44100 -b:a 192k ../data_store/peaks/episode_99.mp3
-./cli.py peaks parse -i original.wav
 ```
 
 ## Running the site
diff --git a/animism-align/cli/app/peaks/extract.py b/animism-align/cli/app/peaks/extract.py
deleted file mode 100644
index dda09d2..0000000
--- a/animism-align/cli/app/peaks/extract.py
+++ /dev/null
@@ -1,31 +0,0 @@
-
-def extract_peaks(fp_in):
-  import os
-  import math
-  import librosa
-  import numpy
-  import json
-
-  from app.settings import app_cfg
-
-  if not os.path.exists(fp_in):
-    print(f"fp_in does not exist: {fp_in}")
-    return
-
-  print(f"Loading {fp_in}")
-  y, sr = librosa.load(fp_in, sr=None)
-
-  sr_10 = sr / 10
-  steps = math.ceil(y.shape[0] / sr_10)
-
-  peaks = numpy.ndarray(steps)
-
-  for i in range(steps):
-    offset_start = math.floor(i * sr_10)
-    offset_end = math.ceil((i + 1) * sr_10)
-    slice = y[offset_start:offset_end]
-    peak = max(abs(slice.min()), slice.max())
-    peaks[i] = float('%.3f' % peak)
-    # peaks[i * 2 + 1] = float('%.3f' % slice.max())
-
-  return peaks.tolist()
diff --git a/animism-align/cli/commands/peaks/parse.py b/animism-align/cli/commands/peaks/parse.py
index 4fd6378..1740c44 100644
--- a/animism-align/cli/commands/peaks/parse.py
+++ b/animism-align/cli/commands/peaks/parse.py
@@ -11,11 +11,38 @@ import click
 def cli(ctx, fp_in):
   """Extract peaks from an audio file.
   """
+  print("This script is deprecated.  Extract peaks using the web UI!")
+  return
 
-  from app.peaks import extract_peaks
-  peaks = extract_peaks(fp_in)
+  import os
+  import math
+  import librosa
+  import numpy
+  import json
+
+  from app.settings import app_cfg
+
+  if not os.path.exists(fp_in):
+    print(f"fp_in does not exist: {fp_in}")
+    return
+
+  print(f"Loading {fp_in}")
+  y, sr = librosa.load(fp_in, sr=None)
+
+  sr_10 = sr / 10
+  steps = math.ceil(y.shape[0] / sr_10)
+
+  peaks = numpy.ndarray(steps)
+
+  for i in range(steps):
+    offset_start = math.floor(i * sr_10)
+    offset_end = math.ceil((i + 1) * sr_10)
+    slice = y[offset_start:offset_end]
+    peak = max(abs(slice.min()), slice.max())
+    peaks[i] = float('%.3f' % peak)
+    # peaks[i * 2 + 1] = float('%.3f' % slice.max())
 
   with open(os.path.join(app_cfg.DIR_DATA_STORE, 'peaks/peaks.json'), 'w') as fp_out:
-    json.dump(peaks, fp_out, separators=(',', ':'))
+    json.dump(peaks.tolist(), fp_out, separators=(',', ':'))
 
   print("Wrote peaks.json")