"""
utils.py — pure logic helpers with no Streamlit dependency.
Covers:
- Audio processing (processFile, clip extraction, randomization)
- DataFrame builders (build_df2 … build_df5)
- Plotly figure builders (one function per chart tab)
- Multi-file summary DataFrame builders
"""
import io
import random
import datetime as dt
import copy
import numpy as np
import pandas as pd
import soundfile as sf
import torch
import plotly.express as px
import plotly.graph_objects as go
import sonogram_utility as su
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
CLIP_MIN_S = 3.0
CLIP_MAX_S = 5.0
CLIP_SCAN_STEP_S = 0.5
TRANSPARENT_BG = dict(
plot_bgcolor="rgba(0,0,0,0)",
paper_bgcolor="rgba(0,0,0,0)",
)
# ---------------------------------------------------------------------------
# Audio processing
# ---------------------------------------------------------------------------
def processFile(filePath, pipeline, enableDenoise, earlyCleanup,
gainWindow, minimumGain, maximumGain,
dfModel=None, dfState=None, attenLimDB=3):
"""Load, optionally denoise, equalize, and diarize an audio file.
Returns (annotations, totalTimeInSeconds, waveform_tensor, sample_rate).
"""
print("Loading file")
waveformList, sampleRate = su.splitIntoTimeSegments(filePath, 600)
print("File loaded")
enhancedWaveformList = []
if enableDenoise:
print("Denoising")
for w in waveformList:
if enableDenoise:
from df import enhance
newW = enhance(dfModel, dfState, w, atten_lim_db=attenLimDB).detach().cpu()
enhancedWaveformList.append(newW)
else:
enhancedWaveformList.append(w)
if enableDenoise:
print("Audio denoised")
waveformEnhanced = su.combineWaveforms(enhancedWaveformList)
if earlyCleanup:
del enhancedWaveformList
print("Equalizing Audio")
waveform_gain_adjusted = su.equalizeVolume()(
waveformEnhanced, sampleRate, gainWindow, minimumGain, maximumGain
)
if earlyCleanup:
del waveformEnhanced
print("Audio Equalized")
print("Detecting speakers")
diarization_output = pipeline({"waveform": waveform_gain_adjusted, "sample_rate": sampleRate})
annotations = diarization_output.speaker_diarization
print("Speakers Detected")
totalTimeInSeconds = int(waveform_gain_adjusted.shape[-1] / sampleRate)
return annotations, totalTimeInSeconds, waveform_gain_adjusted, sampleRate
# ---------------------------------------------------------------------------
# Speaker clip helpers
# ---------------------------------------------------------------------------
def extract_clip_bytes(waveform, sample_rate, seg_start, seg_end):
"""Return WAV bytes for the loudest CLIP_MIN–CLIP_MAX window in [seg_start, seg_end]."""
total_samples = waveform.shape[-1]
seg_start_s = int(seg_start * sample_rate)
seg_end_s = min(int(seg_end * sample_rate), total_samples)
seg_dur = (seg_end_s - seg_start_s) / sample_rate
clip_dur = min(max(min(seg_dur, CLIP_MAX_S), CLIP_MIN_S), seg_dur)
clip_samples = int(clip_dur * sample_rate)
best_start = seg_start_s
best_rms = -1.0
step_samples = int(CLIP_SCAN_STEP_S * sample_rate)
pos = seg_start_s
while pos + clip_samples <= seg_end_s:
window = waveform[:, pos: pos + clip_samples].float()
rms = float(window.pow(2).mean().sqrt())
if rms > best_rms:
best_rms = rms
best_start = pos
pos += step_samples
clip_np = waveform[:, best_start: best_start + clip_samples].numpy().T
buf = io.BytesIO()
sf.write(buf, clip_np, sample_rate, format="WAV", subtype="PCM_16")
buf.seek(0)
return buf.read()
def build_speaker_clips(annotations, waveform, sample_rate):
"""Return (clips_dict, segments_dict) for all speakers in annotations.
clips_dict : {speaker: wav_bytes}
segments_dict : {speaker: [(start, end), ...]}
"""
clips = {}
segments = {}
for speaker in annotations.labels():
speaker_segments = [
seg for seg, _, label in annotations.itertracks(yield_label=True)
if label == speaker
]
if not speaker_segments:
continue
segments[speaker] = [(s.start, s.end) for s in speaker_segments]
longest = max(speaker_segments, key=lambda s: s.duration)
clips[speaker] = extract_clip_bytes(waveform, sample_rate, longest.start, longest.end)
return clips, segments
def get_randomized_clip(waveform, sample_rate, segments):
"""Return WAV bytes for a random 3–5 s window drawn from a random segment.
segments : [(start, end), ...] (all segments for one speaker)
"""
durations = [max(e - s, 0.01) for s, e in segments]
total_dur = sum(durations)
rand_val = random.random() * total_dur
cumulative = 0.0
chosen_start, chosen_end = segments[0]
for (seg_s, seg_e), dur in zip(segments, durations):
cumulative += dur
if rand_val <= cumulative:
chosen_start, chosen_end = seg_s, seg_e
break
seg_dur = chosen_end - chosen_start
clip_dur = min(max(min(seg_dur, CLIP_MAX_S), CLIP_MIN_S), seg_dur)
max_offset = max(seg_dur - clip_dur, 0.0)
offset = random.uniform(0.0, max_offset)
clip_start = chosen_start + offset
clip_end = clip_start + clip_dur
return extract_clip_bytes(waveform, sample_rate, clip_start, clip_end)
# ---------------------------------------------------------------------------
# DataFrame builders (called from analyze() in state.py)
# ---------------------------------------------------------------------------
def build_df3(noVoice, oneVoice, multiVoice):
"""Voice category totals DataFrame."""
return pd.DataFrame({
"values": [su.sumTimes(noVoice), su.sumTimes(oneVoice), su.sumTimes(multiVoice)],
"names": ["No Voice", "One Voice", "Multi Voice"],
})
def build_df4(speakerNames, categorySelections, categoryNames, currAnnotation):
"""Speaker-to-category time DataFrame. Returns (df4, nameList, valueList, extraNames, extraValues)."""
nameList = list(categoryNames)
valueList = [0.0] * len(nameList)
extraNames : list = []
extraValues: list = []
for sp in speakerNames:
found = False
for i, _ in enumerate(nameList):
if sp in categorySelections[i]:
valueList[i] += su.sumTimes(currAnnotation.subset([sp]))
found = True
break
if not found:
extraNames.append(sp)
extraValues.append(su.sumTimes(currAnnotation.subset([sp])))
if extraNames:
pairs = sorted(zip(extraNames, extraValues), key=lambda p: p[0])
extraNames, extraValues = map(list, zip(*pairs))
else:
extraNames, extraValues = [], []
df4 = pd.DataFrame({"values": valueList + extraValues, "names": nameList + extraNames})
return df4, nameList, valueList, extraNames, extraValues
def build_df5(oneVoice, multiVoice, sumNoVoice, sumOneVoice, sumMultiVoice, currTotalTime):
"""Hierarchical voice-category DataFrame for sunburst / treemap."""
speakerList, timeList = su.sumTimesPerSpeaker(oneVoice)
multiSpeakerList, multiTimeList = su.sumMultiTimesPerSpeaker(multiVoice)
speakerList = list(speakerList) if speakerList else []
timeList = list(timeList) if timeList else []
multiSpeakerList = list(multiSpeakerList) if multiSpeakerList else []
multiTimeList = list(multiTimeList) if multiTimeList else []
summativeMulti = sum(multiTimeList) if multiTimeList else 1
safeOneVoice = sumOneVoice if sumOneVoice > 0 else 1
base = [sumNoVoice / currTotalTime, sumOneVoice / currTotalTime, sumMultiVoice / currTotalTime]
timeStrings = su.timeToString(timeList) if timeList else []
multiTimeStrings = su.timeToString(multiTimeList) if multiTimeList else []
if isinstance(timeStrings, str):
timeStrings = [timeStrings]
if isinstance(multiTimeStrings, str):
multiTimeStrings = [multiTimeStrings]
n_ov = len(speakerList)
n_mv = len(multiSpeakerList)
return pd.DataFrame({
"ids": ["NV", "OV", "MV"] + [f"OV_{i}" for i in range(n_ov)] + [f"MV_{i}" for i in range(n_mv)],
"labels": ["No Voice", "One Voice", "Multi Voice"] + speakerList + multiSpeakerList,
"parents": ["", "", ""] + ["OV"] * n_ov + ["MV"] * n_mv,
"parentNames": ["Total", "Total", "Total"] + ["One Voice"] * n_ov + ["Multi Voice"] * n_mv,
"values": [sumNoVoice, sumOneVoice, sumMultiVoice] + timeList + multiTimeList,
"valueStrings": [
su.timeToString(sumNoVoice),
su.timeToString(sumOneVoice),
su.timeToString(sumMultiVoice),
] + timeStrings + multiTimeStrings,
"percentiles": [b * 100 for b in base]
+ [(t * 100) / safeOneVoice * base[1] for t in timeList]
+ [(t * 100) / summativeMulti * base[2] for t in multiTimeList],
"parentPercentiles": [b * 100 for b in base]
+ [(t * 100) / safeOneVoice for t in timeList]
+ [(t * 100) / summativeMulti for t in multiTimeList],
})
def build_df2(df4_names, df4_values, currTotalTime):
"""Percentage-of-total DataFrame (used by the bar chart tab)."""
return pd.DataFrame({
"values": [100 * v / currTotalTime for v in df4_values],
"names": df4_names,
})
# ---------------------------------------------------------------------------
# Plotly figure builders
# ---------------------------------------------------------------------------
def _save_fig(fig, *paths):
"""Try to write fig to each path; silently skip on failure."""
for path in paths:
try:
fig.write_image(path)
except Exception:
pass
def build_fig_pie1(df3, catTypeColors):
"""Voice category pie chart."""
fig = go.Figure()
fig.update_layout(
title_text="Percentage of each Voice Category",
colorway=catTypeColors,
**TRANSPARENT_BG,
)
fig.add_trace(go.Pie(values=df3["values"], labels=df3["names"], sort=False))
return fig
def build_fig_pie2(df4, speakerNames, speakerColors, catColors, get_display_name_fn, currFile):
"""Speaker / category pie chart."""
df4 = df4.copy()
figColors = [
speakerColors[list(speakerNames).index(n)]
for n in df4["names"] if n in speakerNames
]
df4["names"] = df4["names"].apply(lambda s: get_display_name_fn(s, currFile))
fig = go.Figure()
fig.update_layout(
title_text="Percentage of Speakers and Custom Categories",
colorway=catColors + figColors,
**TRANSPARENT_BG,
)
fig.add_trace(go.Pie(values=df4["values"], labels=df4["names"], sort=False))
return fig
def build_fig_sunburst(df5, catTypeColors, speakerColors, get_display_name_fn, currFile):
"""Sunburst voice-category chart."""
df5 = df5.copy()
df5["labels"] = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
fig = px.sunburst(
df5,
branchvalues="total",
names="labels", ids="ids", parents="parents",
values="percentiles",
custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
color="labels",
title="Percentage of each Voice Category with Speakers",
color_discrete_sequence=catTypeColors + speakerColors,
)
fig.update_traces(hovertemplate="
".join([
"%{customdata[0]}",
"Duration: %{customdata[1]}s",
"Percentage of Total: %{customdata[2]:.2f}%",
"Parent: %{customdata[3]}",
"Percentage of Parent: %{customdata[4]:.2f}%",
]))
fig.update_layout(**TRANSPARENT_BG)
return fig
def build_fig_treemap(df5, catTypeColors, speakerColors, get_display_name_fn, currFile):
"""Treemap voice-category chart."""
df5 = df5.copy()
df5["labels"] = df5["labels"].apply(lambda s: get_display_name_fn(s, currFile))
df5["parentNames"] = df5["parentNames"].apply(lambda s: get_display_name_fn(s, currFile))
fig = px.treemap(
df5,
branchvalues="total",
names="labels", parents="parents", ids="ids",
values="percentiles",
custom_data=["labels", "valueStrings", "percentiles", "parentNames", "parentPercentiles"],
color="labels",
title="Division of Speakers in each Voice Category",
color_discrete_sequence=catTypeColors + speakerColors,
)
fig.update_traces(hovertemplate="
".join([
"%{customdata[0]}",
"Duration: %{customdata[1]}s",
"Percentage of Total: %{customdata[2]:.2f}%",
"Parent: %{customdata[3]}",
"Percentage of Parent: %{customdata[4]:.2f}%",
]))
fig.update_layout(**TRANSPARENT_BG)
return fig
def build_fig_timeline(speakers_dataFrame, currTotalTime, speakerColors, get_display_name_fn, currFile):
"""Gantt-style speaker timeline."""
df = speakers_dataFrame.copy()
df["Resource"] = df["Resource"].apply(lambda s: get_display_name_fn(s, currFile))
base = dt.datetime.combine(dt.date.today(), dt.time.min)
def to_audio_dt(s):
if isinstance(s, (dt.datetime, pd.Timestamp)):
midnight = s.replace(hour=0, minute=0, second=0, microsecond=0)
seconds = (s - midnight).total_seconds()
else:
seconds = float(s)
return base + dt.timedelta(seconds=seconds)
df["Start"] = df["Start"].apply(to_audio_dt)
df["Finish"] = df["Finish"].apply(to_audio_dt)
fig = px.timeline(
df, x_start="Start", x_end="Finish", y="Resource", color="Resource",
title="Timeline of Audio with Speakers",
color_discrete_sequence=speakerColors,
)
fig.update_yaxes(autorange="reversed")
h = int(currTotalTime // 3600)
m = int(currTotalTime % 3600 // 60)
s = int(currTotalTime % 60)
ms= int(currTotalTime * 1_000_000 % 1_000_000)
time_max = dt.time(h, m, s, ms)
fig.update_layout(
xaxis_tickformatstops=[
dict(dtickrange=[None, 1000], value="%H:%M:%S.%L"),
dict(dtickrange=[1000, None], value="%H:%M:%S"),
],
xaxis=dict(range=[
dt.datetime.combine(dt.date.today(), dt.time.min),
dt.datetime.combine(dt.date.today(), time_max),
]),
xaxis_title="Time",
yaxis_title="Speaker",
legend_title=None,
legend={"traceorder": "reversed"},
yaxis={"showticklabels": False},
**TRANSPARENT_BG,
)
return fig
def build_fig_bar(df2, catColors, speakerColors, get_display_name_fn, currFile):
"""Horizontal bar chart — time spoken per speaker."""
df2 = df2.copy()
df2["names"] = df2["names"].apply(lambda s: get_display_name_fn(s, currFile))
fig = px.bar(
df2, x="values", y="names", color="names", orientation="h",
custom_data=["names", "values"],
title="Time Spoken by each Speaker",
color_discrete_sequence=catColors + speakerColors,
)
fig.update_xaxes(ticksuffix="%")
fig.update_yaxes(autorange="reversed")
fig.update_layout(
xaxis_title="Percentage Time Spoken",
yaxis_title=None,
showlegend=False,
yaxis={"showticklabels": True},
**TRANSPARENT_BG,
)
fig.update_traces(hovertemplate="
".join([
"%{customdata[0]}",
"Percentage of Time: %{customdata[1]:.2f}%",
]))
return fig
# ---------------------------------------------------------------------------
# Multi-file summary DataFrames
# ---------------------------------------------------------------------------
def build_multifile_category_df(validNames, results, summaries, categories, categorySelect):
"""Build df6 (category breakdown per file) for the multi-file expander."""
df6_dict = {"files": validNames}
allCategories = copy.deepcopy(categories)
for fn in validNames:
currAnnotation, _ = results[fn]
catSummary, extraCats = su.calcCategories(currAnnotation, categorySelect[fn])
summaries[fn]["categories"] = (catSummary, extraCats)
for extra in extraCats:
df6_dict.setdefault(extra, [])
if extra not in allCategories:
allCategories.append(extra)
for category in categories:
df6_dict.setdefault(category, [])
for fn in validNames:
summary, extras = summaries[fn]["categories"]
theseCategories = categories + extras
for j, timeSlots in enumerate(summary):
df6_dict[theseCategories[j]].append(
sum(t.duration for _, t in timeSlots) / results[fn][1]
)
for category in allCategories:
if category not in theseCategories:
df6_dict[category].append(0)
return pd.DataFrame(df6_dict), allCategories
def build_multifile_voice_df(validNames, summaries):
"""Build df7 (no/one/multi voice percentages per file) for the multi-file expander."""
voiceNames = ["No Voice", "One Voice", "Multi Voice"]
df7_dict = {"files": validNames}
for name in voiceNames:
df7_dict[name] = []
for fn in validNames:
partial = summaries[fn]["df5"]
for i, name in enumerate(voiceNames):
df7_dict[name].append(partial["percentiles"][i])
return pd.DataFrame(df7_dict), voiceNames