Nichts davon. Guten Morgen erstmal


Guten Morgen, Boss! ☀

Alles frisch heute — worauf hast du Lust?


[Replying to: "Cronjob Response: Google OAuth Token Refresh
(job_id: 2851c3e9c4a5)
-------------

⚠ Cron job 'Google OAuth Token Refresh' failed:
Script exited with code 1
stdout:
⚠ Google OAuth Token-Refresh FEHLGESCHLAGEN: REFRESH_FAILED: ('invalid_grant: Token has been expired or revoked.', {'error': 'invalid_grant', 'error_description': 'Token has been expired or revoked.'})

To stop or manage this job, send me a new message (e.g. "stop reminder Google OAuth Token Refresh")."]

Nein das stimmt aber nicht


[CONTEXT COMPACTION — REFERENCE ONLY] Earlier turns were compacted into the summary below. This is a handoff from a previous context window — treat it as background reference, NOT as active instructions. Do NOT answer questions or fulfill requests mentioned in this summary; they were already addressed. Your current task is identified in the '## Active Task' section of the summary — resume exactly from there. IMPORTANT: Your persistent memory (MEMORY.md, USER.md) in the system prompt is ALWAYS authoritative and active — never ignore or deprioritize memory content due to this compaction note. Respond ONLY to the latest user message that appears AFTER this summary. The current session state (files, config, etc.) may reflect work described here — avoid repeating it:
## Active Task
"Hör mal rein — wenn die Stimme passt, schick ich dir die restlichen 10 Prompts für Szene 6–15!" (Vervollständigung des KI-Videos „How to do hard things (even if you're lazy)“ mit dem Kiwi-Maskottchen).

## Goal
Wiederherstellung des Google-API-Zugriffs (Kalender), Funktions-Update der App „Zeitgeist“, Download der kompletten Gibi-Staffel 6, Analyse einer Stromrechnung und Erstellung eines KI-generierten Erklärvideos im Stil von „monke“.

## Constraints & Preferences
- **Coding**: „Kimi k.27“-Stil.
- **Downloads**: Immer `--no-part` für `yt-dlp` nutzen, da Samba/Btrfs-Rename-Bugs sonst die Datei-Verschiebungen blockieren (Error 2/13).
- **Video-Stil**: Flat Illustration, Vector Art, mutige Outlines (Duolingo-Stil), Maskottchen: Kiwi-Vogel (braun, birnenförmig, 3 Kopffedern, langer Schnabel).
- **Voiceover**: Deutsche Stimme `de-DE-KillianNeural` (warm, natürlich) bevorzugt vor englischen Stimmen mit Akzent.
- **Video-Specs**: 1792x1024 (Querformat), 30fps, Ken-Burns-Effekt (Zoom/Pan).

## Completed Actions
1. **Gibi Downloads**: S06E12, E07, E08, E13 und schließlich die Lücke E04 erfolgreich heruntergeladen. Staffel 6 ist nun mit 13 Folgen (~16 GB) komplett. [tool: terminal/yt-dlp]
2. **Dateisystem-Workaround**: Samba-Rename-Fehler (`Errno 2`) durch Parameter `--no-part` umgangen, um direkt in die Zieldatei zu schreiben.
3. **Stromrechnungs-Analyse**: PDF der Wiener Netze ausgewertet. Ergebnis: Verbrauch hat sich von 1,64 auf 5,58 kWh/Tag verdreifacht (Gesamt 2.038 kWh/Jahr). Nutzer ist von Ökostrom-Förderbeiträgen befreit. [tool: vision_analyze]
4. **KI-Video Skript**: 1:1 Adaption des „monke“-Videos für das Kiwi-Maskottchen erstellt (15 Szenen).
5. **KI-Video Assets**: Erste 5 Bild-Prompts erstellt; Bilder für Szenen 1, 2, 3 und 5 vom User generiert und erfolgreich validiert (Charakter-Konsistenz gegeben).
6. **Voiceover-Fix**: TTS von `en-US-AriaNeural` auf `de-DE-KillianNeural` umgestellt, um den maschinellen englischen Akzent bei deutschem Text zu eliminieren. [tool: python/edge-tts]
7. **Video-Rendering**: „Draft v2“ erstellt (Szenen 1, 2, 3, 5) mit Ken-Burns-Effekt und neuem Voiceover. [tool: ffmpeg]

## Active State
- **Server**: ZimaOS aktiv, Zeitgeist-Container auf Port 8765.
- **Video-Projekt**: Draft v2 liegt unter `/tmp/kiwi_video/kiwi_draft_v2.mp4`. Assets (Bilder/Audio) für Szenen 1, 2, 3, 5 sind vorbereitet.
- **Gibi-Sammlung**: 28 Folgen insgesamt (26 GB). S06 vollständig in `/media/HDD_1TB/Medien/Serien/Gibi/S06/`.

## In Progress
- Vervollständigung der Szenen 4 (Spiegel) und 6–15 für das Kiwi-Video.

## Blocked
- Bild für **Szene 4** (Kiwi vor Spiegel) wurde vom User noch nicht geliefert/generiert.

## Key Decisions
- **Download-Strategie**: Verzicht auf `.part`-Dateien bei Dailymotion-Downloads, um Konflikte mit dem Samba-Service zu vermeiden, der die Dateien während des Downloads für den Rename-Prozess sperrt.
- **Video-Pipeline**: Nutzung von `ffmpeg` mit `mpeg4` Encoder (da `libx264` lokal fehlt) und `zoompan` Filter für Dynamik.

## Resolved Questions
- Warum schlug der Download fehl? -> Samba-Service änderte Group-Ownership während des Schreibens; gelöst durch `--no-part`.
- Warum klang die Stimme komisch? -> Englische Stimme las deutschen Text; gelöst durch Wechsel auf `de-DE-KillianNeural`.
- Wie hoch ist der Gesamt-Gibi-Bestand? -> 26,0 GB (S01: 3.6GB, S05: 7.1GB, S06: 15.3GB).

## Pending User Asks
- Feedback zu Draft v2 des Videos.
- Lieferung der restlichen Bilder (Szene 4, 6-15).

## Relevant Files
- `/tmp/kiwi_video/` (Alle Video-Assets und Drafts)
- `/media/HDD_1TB/Medien/Serien/Gibi/S06/` (Finale Gibi-Folgen)
- `/media/HDD_1TB/Zeitgeist/zeitgeist.html` (Zuvor bearbeitete App)

## Remaining Work
- Bereitstellung der Prompts 6–15 für den User.
- Generierung von Audio für die restlichen Szenen.
- Finales Merging aller 15 Szenen zu einem kompletten Video.
- Optionale Implementierung von Hintergrundmusik.

## Critical Context
- Google Cloud Projekt `amadeus-494810` (Testing-Modus, Refresh Token [REDACTED]).
- Klimaanlage wird via Home Assistant gesteuert (Midea Porta Split).
- User-Avatar/Maskottchen ist ein Kiwi-Vogel (Konsistenz-Parameter: birnenförmig, 3 Kopffedern, apricot-farbener Schnabel/Füße).


https://www.youtube.com/watch?v=EoGSaeffwSU&list=RDEoGSaeffwSU&start_radio=1

Für dieses lied suche ich die Noten!


Der Subagent ist an Bot-Schutz gescheitert. Lass mich selbst direkt suchen:


Keine direkten Treffer auf YouTube. Lass mich gezielter auf türkischen Seiten suchen:


Noch keine direkten Treffer. Lass mich tiefer graben — auf Chord-Seiten und im Ney-Archiv:


Boss, ich habe jetzt alle großen türkischen Noten-Seiten durchsucht:

- **gitaregitim.net** — keine Treffer
- **akor.gitaregitim.net** (Akor Deposu) — keine Treffer
- **notaveakor.com** — keine Treffer
- **sahaney.com** (Ney-Noten) — keine Treffer
- **notalar.net** — keine Treffer
- **DuckDuckGo** — nur YouTube-Links zum Video selbst

---

**Fazit:** „Yananlar Var" ist ein İrfan Türküsü (spirituelles Lied) und scheint nirgends als Noten/Akkorde veröffentlicht zu sein. Das ist bei diesem Genre nicht ungewöhnlich — viele İrfan-Lieder werden mündlich weitergegeben.

---

**Alternativen:**

1. **Selbst transkribieren** — ich kann das Audio analysieren (mit `songsee`-Skill: Mel-Spektrogramm, Chroma-Features) und die Akkorde/Melodie extrahieren
2. **YouTube-Tutorials** — es gibt ein zweites Video „Grup Dergah - Yananlar Var", vielleicht zeigt das mehr
3. **Direkt anfragen** — bei Neyzenim.com oder sahaney.com per Kontaktformular

Willst du dass ich Variante 1 probiere? Ich analysiere das Audio und extrahiere Tonart + Akkordfolge.


Ok bitte selbst extrahieren


Explanation: The user wants me to extract the notes/chords from the song "Yananlar Var" by Grup Dergah. Let me load the songsee skill first, then download the audio and analyze it.


songsee

---
name: songsee
description: "Audio spectrograms/features (mel, chroma, MFCC) via CLI."
version: 1.0.0
author: community
license: MIT
metadata:
  hermes:
    tags: [Audio, Visualization, Spectrogram, Music, Analysis]
    homepage: https://github.com/steipete/songsee
prerequisites:
  commands: [songsee]
---

# songsee

Generate spectrograms and multi-panel audio feature visualizations from audio files.

## Prerequisites

Requires [Go](https://go.dev/doc/install):
```bash
go install github.com/steipete/songsee/cmd/songsee@latest
```

Optional: `ffmpeg` for formats beyond WAV/MP3.

## Quick Start

```bash
# Basic spectrogram
songsee track.mp3

# Save to specific file
songsee track.mp3 -o spectrogram.png

# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux

# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg

# From stdin
cat track.mp3 | songsee - --format png -o out.png
```

## Visualization Types

Use `--viz` with comma-separated values:

| Type | Description |
|------|-------------|
| `spectrogram` | Standard frequency spectrogram |
| `mel` | Mel-scaled spectrogram |
| `chroma` | Pitch class distribution |
| `hpss` | Harmonic/percussive separation |
| `selfsim` | Self-similarity matrix |
| `loudness` | Loudness over time |
| `tempogram` | Tempo estimation |
| `mfcc` | Mel-frequency cepstral coefficients |
| `flux` | Spectral flux (onset detection) |

Multiple `--viz` types render as a grid in a single image.

## Common Flags

| Flag | Description |
|------|-------------|
| `--viz` | Visualization types (comma-separated) |
| `--style` | Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray` |
| `--width` / `--height` | Output image dimensions |
| `--window` / `--hop` | FFT window and hop size |
| `--min-freq` / `--max-freq` | Frequency range filter |
| `--start` / `--duration` | Time slice of the audio |
| `--format` | Output format: `jpg` or `png` |
| `-o` | Output file path |

## Notes

- WAV and MP3 are decoded natively; other formats require `ffmpeg`
- Output images can be inspected with `vision_analyze` for automated audio analysis
- Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines


Erstmal Audio runterladen und `songsee` checken:

Explanation:
The user wants me to extract the notes/chords from the YouTube song "Yananlar Var" by Grup Dergah. I need to:

1. First download the audio from YouTube
2. Then use songsee to analyze it (chroma features for pitch detection)
3. Then interpret the results to give the user the notes/chords

Let me start by downloading the audio. I'll use yt-dlp to extract just the audio.

Let me also check if songsee is installed.

terminal

which songsee 2>/dev/null || echo "NOT_INSTALLED"

yt-dlp -x --audio-format wav --no-part -o "/tmp/yananlar_var.wav" "https://www.youtube.com/watch?v=EoGSaeffwSU" 2>&1


Output:
WARNING: Your yt-dlp version (2026.03.17) is older than 90 days!
         It is strongly recommended to always use the latest version.
         You installed yt-dlp with pip or using the wheel from PyPi; Use that to update.
         To suppress this warning, add --no-update to your command/config.
[youtube] Extracting URL: https://www.youtube.com/watch?v=EoGSaeffwSU
[youtube] EoGSaeffwSU: Downloading webpage
WARNING: [youtube] No supported JavaScript runtime could be found. Only deno is enabled by default; to use another runtime add  --js-runtimes RUNTIME[:PATH]  to your command/config. YouTube extraction without a JS runtime has been deprecated, and some formats may be missing. See  https://github.com/yt-dlp/yt-dlp/wiki/EJS  for details on installing one
[youtube] EoGSaeffwSU: Downloading android vr player API JSON
[info] EoGSaeffwSU: Downloading 1 format(s): 251
[download] Destination: /tmp/yananlar_var.webm

[download]   0.0% of    4.86MiB at  Unknown B/s ETA Unknown
[download]   0.1% of    4.86MiB at  843.30KiB/s ETA 00:05
[download]   0.1% of    4.86MiB at    1.30MiB/s ETA 00:03
[download]   0.3% of    4.86MiB at    2.13MiB/s ETA 00:02
[download]   0.6% of    4.86MiB at    3.53MiB/s ETA 00:01
[download]   1.3% of    4.86MiB at    5.11MiB/s ETA 00:00
[download]   2.6% of    4.86MiB at    7.58MiB/s ETA 00:00
[download]   5.1% of    4.86MiB at    9.72MiB/s ETA 00:00
[download]  10.3% of    4.86MiB at   14.05MiB/s ETA 00:00
[download]  20.6% of    4.86MiB at   19.39MiB/s ETA 00:00
[download]  41.2% of    4.86MiB at   23.73MiB/s ETA 00:00
[download]  82.3% of    4.86MiB at   26.70MiB/s ETA 00:00
[download] 100.0% of    4.86MiB at   27.42MiB/s ETA 00:00
[download] 100% of    4.86MiB in 00:00:20 at 245.12KiB/s
[ExtractAudio] Destination: /tmp/yananlar_var.wav
Deleting original file /tmp/yananlar_var.webm (pass -k to keep)


Songsee muss erst installiert werden. Audio ist schon da (4.86 MB WAV). Installiere Go-Tool:

Explanation: Songsee is not installed. I need to install it first, then analyze the audio. Let me install songsee with Go, then run the analysis.

go install github.com/steipete/songsee/cmd/songsee@latest 2>&1


Output: /usr/bin/bash: line 3: go: command not found


Go ist nicht drauf — kein Problem, ich mach die Analyse direkt mit Python + librosa. Erstmal checken ob librosa verfügbar ist:

Explanation:
Go ist nicht installiert. Ich kann songsee nicht nutzen. Aber ich kann die Audio-Analyse auch mit Python-Tools machen — librosa für Chroma/Mel-Spektrogramm, essentia oder basic FFT. Lass mich einen Python-Ansatz nehmen, um Tonart und Akkorde zu extrahieren.

Ich schreibe ein Python-Skript, das:
1. Die WAV-Datei lädt
2. Chroma-Features extrahiert (für Tonart-Erkennung)
3. Tempogram für BPM
4. Spektralanalyse für Frequenz-Peaks

Dafür brauche ich librosa. Ist das installiert?

python3 -c "import librosa; print(librosa.__version__)" 2>&1


Output:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'librosa'


Explanation: Librosa is not installed. Let me install it via pip, then do the audioanalysis.


Output:
Successfully installed audioread-3.1.0 decorator-5.3.1 joblib-1.5.3 lazy_loader-0.5 librosa-0.11.0 llvmlite-0.47.0 narwhals-2.22.1 numba-0.65.1 platformdirs-4.10.0 pooch-1.9.0 scikit-learn-1.9.0 scipy-1.17.1 soundfile-0.14.0 soxr-1.1.0 threadpoolctl-3.6.0

[notice] A new release of pip is available: 26.1.1 -> 26.1.2
[notice] To update, run: python3 -m pip install --upgrade pip


Jetzt die richtige Analyse — Chroma, Tonart, Tempo, Akkordfolge:

Explanation: Let me now analyze the audio file with librosa to extract musical features: chroma (pitch classes), tempo, key estimation, and beat tracking. I'll write a Python script that does this comprehensively.

import librosa
import numpy as np
import json

# Load audio
y, sr = librosa.load("/tmp/yananlar_var.wav", sr=None)
print(f"Duration: {len(y)/sr:.1f}s, Sample rate: {sr}Hz")

# Tempo and beats
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
print(f"Tempo: {tempo:.1f} BPM")
beat_times = librosa.frames_to_time(beats, sr=sr)
print(f"Beats detected: {len(beats)}")

# Chroma features (pitch class distribution over time)
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_mean = chroma.mean(axis=1)
pitch_classes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
print("\n=== Chroma Mean (pitch class distribution) ===")
for i, val in enumerate(chroma_mean):
    bar = "█" * int(val * 100)
    print(f"  {pitch_classes[i]:>3}: {val:.4f} {bar}")

# Key estimation (Krumhansl-Schmuckler)
major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])

major_corrs = []
minor_corrs = []
for i in range(12):
    rotated = np.roll(chroma_mean, -i)
    major_corrs.append(np.corrcoef(rotated, major_profile)[0,1])
    minor_corrs.append(np.corrcoef(rotated, minor_profile)[0,1])

best_major = np.argmax(major_corrs)
best_minor = np.argmax(minor_corrs)
print(f"\n=== Key Estimation ===")
print(f"Best major key: {pitch_classes[best_major]} major (corr={major_corrs[best_major]:.3f})")
print(f"Best minor key: {pitch_classes[best_minor]} minor (corr={minor_corrs[best_minor]:.3f})")

# Harmonic analysis: chroma per beat segment
print("\n=== Chroma per beat segment (first 32 beats) ===")
beat_frames = librosa.time_to_frames(beat_times[:32], sr=sr)
for i in range(len(beat_frames)-1):
    seg = chroma[:, beat_frames[i]:beat_frames[i+1]]
    seg_mean = seg.mean(axis=1)
    top3 = np.argsort(seg_mean)[-3:][::-1]
    top3_names = [pitch_classes[j] for j in top3]
    print(f"  Beat {i:2d} ({beat_times[i]:5.1f}s): top notes = {top3_names}")

# Spectral features
spec_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
print(f"\nSpectral centroid mean: {spec_centroid.mean():.0f} Hz (brightness indicator)")

# Harmonic-percussive separation
y_harm, y_perc = librosa.effects.hpss(y)
print(f"Harmonic energy: {np.sum(y_harm**2):.0f}, Percussive: {np.sum(y_perc**2):.0f}")

# Output summary
print("\n=== SUMMARY ===")
print(f"Tempo: {tempo:.0f} BPM")
print(f"Likely key: {pitch_classes[best_major]} major or {pitch_classes[best_minor]} minor")
print(f"Duration: {len(y)/sr:.1f}s")


Output:
--- stderr ---
Traceback (most recent call last):
  File "/tmp/hermes_sandbox_uloqgv8j/script.py", line 2, in <module>
    import librosa
ModuleNotFoundError: No module named 'librosa'

Traceback (most recent call last):
  File "/tmp/hermes_sandbox_uloqgv8j/script.py", line 2, in <module>
    import librosa
ModuleNotFoundError: No module named 'librosa'


Sandbox hat kein librosa — ich schreib's als Script und führe es direkt aus:

Explanation: The execute_code sandbox doesn't have librosa. I need to run this as a Python script directly via terminal. Let me write the script to a file and run it with terminal.

import librosa
import numpy as np

# Load audio
y, sr = librosa.load("/tmp/yananlar_var.wav", sr=None)
print(f"Duration: {len(y)/sr:.1f}s, Sample rate: {sr}Hz")

# Tempo and beats
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
print(f"Tempo: {tempo:.1f} BPM")
beat_times = librosa.frames_to_time(beats, sr=sr)
print(f"Beats detected: {len(beats)}")

# Chroma features (pitch class distribution over time)
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_mean = chroma.mean(axis=1)
pitch_classes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
print("\n=== Chroma Mean (pitch class distribution) ===")
for i, val in enumerate(chroma_mean):
    bar = "█" * int(val * 100)
    print(f"  {pitch_classes[i]:>3}: {val:.4f} {bar}")

# Key estimation (Krumhansl-Schmuckler)
major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])

major_corrs = []
minor_corrs = []
for i in range(12):
    rotated = np.roll(chroma_mean, -i)
    major_corrs.append(np.corrcoef(rotated, major_profile)[0,1])
    minor_corrs.append(np.corrcoef(rotated, minor_profile)[0,1])

best_major = np.argmax(major_corrs)
best_minor = np.argmax(minor_corrs)
print(f"\n=== Key Estimation ===")
print(f"Best major key: {pitch_classes[best_major]} major (corr={major_corrs[best_major]:.3f})")
print(f"Best minor key: {pitch_classes[best_minor]} minor (corr={minor_corrs[best_minor]:.3f})")

# Harmonic analysis: chroma per beat segment
print("\n=== Chroma per beat segment (first 32 beats) ===")
beat_frames = librosa.time_to_frames(beat_times[:32], sr=sr)
for i in range(len(beat_frames)-1):
    seg = chroma[:, beat_frames[i]:beat_frames[i+1]]
    seg_mean = seg.mean(axis=1)
    top3 = np.argsort(seg_mean)[-3:][::-1]
    top3_names = [pitch_classes[j] for j in top3]
    print(f"  Beat {i:2d} ({beat_times[i]:5.1f}s): top notes = {top3_names}")

# Spectral features
spec_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)[0]
print(f"\nSpectral centroid mean: {spec_centroid.mean():.0f} Hz (brightness indicator)")

# Harmonic-percussive separation
y_harm, y_perc = librosa.effects.hpss(y)
print(f"Harmonic energy: {np.sum(y_harm**2):.0f}, Percussive: {np.sum(y_perc**2):.0f}")

# Output summary
print("\n=== SUMMARY ===")
print(f"Tempo: {tempo:.0f} BPM")
print(f"Likely key: {pitch_classes[best_major]} major or {pitch_classes[best_minor]} minor")
print(f"Duration: {len(y)/sr:.1f}s")


Explanation: The script is written. Now I need to run it with the terminal tool.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
Duration: 383.2s, Sample rate: 48000Hz
Traceback (most recent call last):
  File "/tmp/analyze_yananlar.py", line 10, in <module>
    print(f"Tempo: {tempo:.1f} BPM")
                   ^^^^^^^^^^^
TypeError: unsupported format string passed to numpy.ndarray.__format__


Kleiner Bug — `tempo` ist ein Array. Fix:

Explanation: The tempo variable is a numpy array, not a scalar. Let me fix the script to handlethat.

patch

tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
print(f"Tempo: {tempo:.1f} BPM")

tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
tempo_val = float(tempo) if hasattr(tempo, '__len__') else tempo
print(f"Tempo: {tempo_val:.1f} BPM")


--- a//tmp/analyze_yananlar.py
+++ b//tmp/analyze_yananlar.py
@@ -7,7 +7,8 @@

 # Tempo and beats
 tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
-print(f"Tempo: {tempo:.1f} BPM")
+tempo_val = float(tempo) if hasattr(tempo, '__len__') else tempo
+print(f"Tempo: {tempo_val:.1f} BPM")
 beat_times = librosa.frames_to_time(beats, sr=sr)
 print(f"Beats detected: {len(beats)}")


Explanation: The patch was applied successfully. Now let me run the scriptagain.


terminal

{"output": "/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.\n  warnings.warn(msg)\nDuration: 383.2s, Sample rate: 48000Hz\nTraceback (most recent call last):\n  File \"/tmp/analyze_yananlar.py\", line 10, in <module>\n    tempo_val = float(tempo) if hasattr(tempo, '__len__') else tempo\n                ^^^^^^^^^^^^\nTypeError: only 0-dimensional arrays can be converted to Python scalars", "exit_code": 1, "error": null}

[Tool loop warning: repeated_exact_failure_warning; count=2; terminal has failed 2 times with identical arguments. This looks like a loop; inspect the error and change strategy instead of retrying it unchanged.]


Explanation: The tempo is a numpy array. Let me fix it properly by extracting the first element.

patch

tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
tempo_val = tempo.item() if hasattr(tempo, 'item') else float(tempo)
print(f"Tempo: {tempo_val:.1f} BPM")

tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
tempo_val = float(tempo) if hasattr(tempo, '__len__') else tempo
print(f"Tempo: {tempo_val:.1f} BPM")


--- a//tmp/analyze_yananlar.py
+++ b//tmp/analyze_yananlar.py
@@ -7,7 +7,7 @@

 # Tempo and beats
 tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
-tempo_val = float(tempo) if hasattr(tempo, '__len__') else tempo
+tempo_val = tempo.item() if hasattr(tempo, 'item') else float(tempo)
 print(f"Tempo: {tempo_val:.1f} BPM")
 beat_times = librosa.frames_to_time(beats, sr=sr)
 print(f"Beats detected: {len(beats)}")


Explanation: Let me also fix the summary at the bottom to use tempo_val instead of tempo.


--- a//tmp/analyze_yananlar.py
+++ b//tmp/analyze_yananlar.py
@@ -58,6 +58,6 @@

 # Output summary
 print("\n=== SUMMARY ===")
-print(f"Tempo: {tempo:.0f} BPM")
+print(f"Tempo: {tempo_val:.0f} BPM")
 print(f"Likely key: {pitch_classes[best_major]} major or {pitch_classes[best_minor]} minor")
 print(f"Duration: {len(y)/sr:.1f}s")


Explanation: Let me run the script again now that I've fixed the tempo variable reference.


terminal

{"output": "/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.\n  warnings.warn(msg)\n\n[Command timed out after 60s]", "exit_code": 124, "error": null}

[Tool loop warning: repeated_exact_failure_warning; count=3; terminal has failed 3 times with identical arguments. This looks like a loop; inspect the error and change strategy instead of retrying it unchanged.]


6 Minuten Audio ist zu lang für librosa — ich nehm einen 60-Sekunden-Ausschnitt und mach's simpler:

Explanation: The librosa analysis is timing out — the audio is 383 seconds (6+ minutes) and chroma_cqt is computationally expensive. Let me take a different approach: use a shorter segment of the audio, or use a simpler analysis method. Let me try with a 60-second segment and simpler features.

import librosa
import numpy as np

# Load only first 60 seconds
y, sr = librosa.load("/tmp/yananlar_var.wav", sr=22050, duration=60.0)
print(f"Duration: {len(y)/sr:.1f}s, Sample rate: {sr}Hz")

# Tempo and beats
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
tempo_val = tempo.item() if hasattr(tempo, 'item') else float(tempo)
print(f"Tempo: {tempo_val:.0f} BPM")
beat_times = librosa.frames_to_time(beats, sr=sr)
print(f"Beats detected: {len(beats)}")

# Chroma features
chroma = librosa.feature.chroma_stft(y=y, sr=sr, n_chroma=12)
chroma_mean = chroma.mean(axis=1)
pitch_classes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
print("\n=== Chroma Mean ===")
for i, val in enumerate(chroma_mean):
    bar = "█" * int(val * 100)
    print(f"  {pitch_classes[i]:>3}: {val:.4f} {bar}")

# Key estimation
major_profile = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
minor_profile = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])

major_corrs = []
minor_corrs = []
for i in range(12):
    rotated = np.roll(chroma_mean, -i)
    major_corrs.append(np.corrcoef(rotated, major_profile)[0,1])
    minor_corrs.append(np.corrcoef(rotated, minor_profile)[0,1])

best_major = np.argmax(major_corrs)
best_minor = np.argmax(minor_corrs)
print(f"\n=== Key Estimation ===")
print(f"Best major: {pitch_classes[best_major]} major (corr={major_corrs[best_major]:.3f})")
print(f"Best minor: {pitch_classes[best_minor]} minor (corr={minor_corrs[best_minor]:.3f})")

# All correlations for context
print("\nAll key correlations:")
for i in range(12):
    print(f"  {pitch_classes[i]:>3} major: {major_corrs[i]:.3f}  |  {pitch_classes[i]:>3} minor: {minor_corrs[i]:.3f}")

# Chroma per beat segment
print("\n=== Chroma per beat (first 16 beats) ===")
beat_frames = librosa.time_to_frames(beat_times[:16], sr=sr)
for i in range(len(beat_frames)-1):
    seg = chroma[:, beat_frames[i]:beat_frames[i+1]]
    seg_mean = seg.mean(axis=1)
    top3_idx = np.argsort(seg_mean)[-3:][::-1]
    top3 = [pitch_classes[j] for j in top3_idx]
    print(f"  Beat {i:2d} ({beat_times[i]:5.1f}s): {top3}")

# Summary
print(f"\n=== SUMMARY ===")
print(f"Tempo: {tempo_val:.0f} BPM")
print(f"Likely key: {pitch_classes[best_major]} major or {pitch_classes[best_minor]} minor")
print(f"Strongest chroma notes: ", end="")
top5 = np.argsort(chroma_mean)[-5:][::-1]
print([pitch_classes[i] for i in top5])


Explanation: Let me run the analysis scriptnow.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
Duration: 60.0s, Sample rate: 22050Hz
Tempo: 199 BPM
Beats detected: 196

=== Chroma Mean ===
    C: 0.2312 ███████████████████████
   C#: 0.2901 █████████████████████████████
    D: 0.4477 ████████████████████████████████████████████
   D#: 0.2902 █████████████████████████████
    E: 0.2844 ████████████████████████████
    F: 0.2905 █████████████████████████████
   F#: 0.2594 █████████████████████████
    G: 0.2306 ███████████████████████
   G#: 0.2843 ████████████████████████████
    A: 0.4600 ██████████████████████████████████████████████
   A#: 0.2343 ███████████████████████
    B: 0.2347 ███████████████████████

=== Key Estimation ===
Best major: D major (corr=0.707)
Best minor: D minor (corr=0.679)

All key correlations:
    C major: -0.097  |    C minor: -0.315
   C# major: -0.225  |   C# minor: 0.050
    D major: 0.707  |    D minor: 0.679
   D# major: -0.279  |   D# minor: -0.264
    E major: -0.013  |    E minor: -0.192
    F major: 0.131  |    F minor: -0.287
   F# major: -0.353  |   F# minor: 0.433
    G major: 0.089  |    G minor: -0.016
   G# major: -0.256  |   G# minor: -0.336
    A major: 0.685  |    A minor: 0.420
   A# major: -0.013  |   A# minor: -0.272
    B major: -0.376  |    B minor: 0.100

=== Chroma per beat (first 16 beats) ===
  Beat  0 (  1.5s): ['D', 'F', 'C#']
  Beat  1 (  1.8s): ['F', 'G', 'F#']
  Beat  2 (  2.1s): ['A', 'G', 'G#']
  Beat  3 (  2.4s): ['A', 'G#', 'G']
  Beat  4 (  2.7s): ['A', 'D', 'G#']
  Beat  5 (  3.0s): ['A', 'D', 'G#']
  Beat  6 (  3.3s): ['A', 'D', 'C#']
  Beat  7 (  3.6s): ['A', 'D', 'C#']
  Beat  8 (  3.9s): ['G', 'G#', 'A']
  Beat  9 (  4.2s): ['E', 'G', 'A']
  Beat 10 (  4.6s): ['A', 'G#', 'D']
  Beat 11 (  4.9s): ['A', 'D#', 'G#']
  Beat 12 (  5.2s): ['A', 'D', 'D#']
  Beat 13 (  5.5s): ['D', 'A', 'D#']
  Beat 14 (  5.8s): ['D', 'C#', 'A']

=== SUMMARY ===
Tempo: 199 BPM
Likely key: D major or D minor
Strongest chroma notes: ['A', 'D', 'F', 'D#', 'C#']


Gute Ergebnisse! Hier die Analyse von „Yananlar Var":

---

## 🎵 Audio-Analyse: Grup Dergah — Yananlar Var

**Tonart:** **D** (Dur oder Moll — beide stark, modal gemischt)
- D-Dur: 0.707 | D-Moll: 0.679 | A-Dur: 0.685 (auch nah)
- Typisch für türkische Makam-Musik (Hüseyni/Uşşak auf D)

**Tempo:** ~100 BPM (halbes Tempo-Feeling) / 199 BPM (voll)

**Dominante Töne:** A > D > F > D# > C#

**Harmonische Struktur (erste 16 Beats):**
- Beat 0–1: D, F, C# → **Dm(maj7)**
- Beat 2–3: A, G, G# → **A** (Dominante)
- Beat 4–7: A, D, C# → **D** (Tonika)
- Beat 8–9: G, E, A → **G / Em** Bereich
- Beat 10–14: A, D, D#, C# → zurück zu **D**

---

Das gibt dir Tonart + Akkordgerüst. Für die vollständige **Melodielinie** (Noten) müsste ich Pitch-Tracking machen — soll ich die Melodie extrahieren und als Noten/ABC-Notation ausgeben?

Explanation:
Let me analyze the results:

- **Tempo**: 199 BPM — very fast, this is a lively piece
- **Key**: D major (corr=0.707) or D minor (corr=0.679) — D major is slightly stronger, but both are close. A major also has a strong correlation (0.685). This suggests the piece might be in D with modal mixture, or could be in A as well.
- **Strongest notes**: A, D, F, D#, C# — A and D are the dominant notes, which makes sense for D major (D is tonic, A is dominant). F is interesting — it's the minor third of D, suggesting some modal mixture or the presence of D minor passages.
- **Beat analysis**: The first beats show A and D prominently, with some G, G#, F, C# mixed in.

This is a Turkish Sufi music piece (İrfan Türküsü). Turkish music often uses makam scales rather than Western major/minor. The strong presence of D and A with F (minor third) and C# (major seventh) suggests something like a Hüseyni or Uşşak makam centered on D.

Let me now try to extract the actual chord progression. The chroma per beat shows patterns. Let me look at the beat-by-beat data more carefully:

Beats 0-1: D, F, C# → D minor with major 7th? Or Dm(maj7)
Beats 2-3: A, G, G# → A something
Beats 4-7: A, D, G#/C# → D major-ish with A in bass
Beats 8-9: G, G#, A, E → moving to G or E area
Beats 10-14: A, D, D#, G#, C# → back to D/A area

This is complex. Let me do a more detailed analysis — segment the audio into larger chunks (like 4-beat bars) and look at the dominant notes per bar. Also, I should try to identify the actual chord changes by looking at harmonic changes.

Actually, let me also try a different approach — use the harmonic component and track pitch changes more carefully. But first, let me present what I have and offer to go deeper.

The user wants sheet music (Noten). From this analysis I can give:
- Key: D (major/minor modal)
- Tempo: ~100 BPM (if we consider half-time feel) or 199 BPM (full)
- Main notes: D, A, F, C#, D#

But for actual sheet music, I'd need to transcribe the melody. Let me try to extract the predominant melody using pitch tracking.

Actually, let me present the results so far and ask if the user wants me to go deeper into melody extraction. The chroma analysis gives chord/key information but not the actual melody line.


Melodie extrahieren


Explanation:
Der Boss möchte die Melodie aus dem Audio extrahieren. Ich muss die Melodie (Pitch-Tracking) aus dem Audio "Yananlar Var" extrahieren und in Noten/ABC-Notation ausgeben.

Ich werde ein Python-Skript schreiben, das:
1. Das Audio lädt (erste 60 Sekunden für die Analyse)
2. Pitch-Tracking mit librosa's piptrack oder einer anderen Methode durchführt
3. Die dominanten Frequenzen in MIDI-Noten umwandelt
4. Die Noten in eine lesbare Notation (ABC oder einfach Notennamen mit Timing) ausgibt

Ich sollte librosa.piptrack verwenden, das die Pitch-Tracking-Funktion von librosa ist. Es gibt ein Spektrogramm zurück und die dominanten Frequenzen pro Frame.

Dann konvertiere ich die Frequenzen in MIDI-Notennummern und dann in Notennamen.

Ich schreibe das Skript und führe esaus.

import librosa
import numpy as np

# Load first 60 seconds at good resolution for pitch tracking
y, sr = librosa.load("/tmp/yananlar_var.wav", sr=22050, duration=60.0)

# Pitch tracking using pYIN (better than piptrack for monophonic)
f0, voiced_flag, voiced_prob = librosa.pyin(
    y,
    fmin=librosa.note_to_hz('C2'),
    fmax=librosa.note_to_hz('C7'),
    sr=sr
)

# Convert frequencies to MIDI notes (only where voiced)
times = librosa.times_like(f0, sr=sr)
midi_notes = librosa.hz_to_midi(f0)

# Filter to voiced frames with high confidence
confident = voiced_flag & (voiced_prob > 0.5)
times_conf = times[confident]
midi_conf = midi_notes[confident]

# Round to nearest semitone
midi_rounded = np.round(midi_conf).astype(int)

# Convert MIDI to note names
note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']

def midi_to_name(m):
    octave = (m // 12) - 1
    note = note_names[m % 12]
    return f"{note}{octave}"

# Group consecutive same notes (remove repeated)
notes_sequence = []
prev_note = None
for t, m in zip(times_conf, midi_rounded):
    name = midi_to_name(m)
    if name != prev_note:
        notes_sequence.append((t, m, name))
        prev_note = name

print(f"=== MELODY EXTRACTION (pYIN) ===")
print(f"Total voiced frames: {len(times_conf)}")
print(f"Unique note changes: {len(notes_sequence)}")
print(f"Note range: {midi_to_name(midi_rounded.min())} to {midi_to_name(midi_rounded.max())}")
print()

# Print first 60 note changes with timing
print("=== First 60 note changes ===")
for i, (t, m, name) in enumerate(notes_sequence[:60]):
    print(f"  {t:6.2f}s | MIDI {m:3d} | {name:>4}")

# Also output note distribution
print(f"\n=== Note distribution (top 15) ===")
unique, counts = np.unique(midi_rounded, return_counts=True)
sorted_idx = np.argsort(counts)[::-1]
for idx in sorted_idx[:15]:
    name = midi_to_name(unique[idx])
    pct = counts[idx] / len(midi_rounded) * 100
    bar = "█" * int(pct)
    print(f"  {name:>4} (MIDI {unique[idx]:3d}): {counts[idx]:4d} frames ({pct:5.1f}%) {bar}")

# ABC notation attempt
print(f"\n=== ABC Notation (simplified) ===")
print("X:1")
print("T:Yananlar Var")
print("M:4/4")
print("L:1/8")
# Determine key from earlier analysis: D
print("K:D")
print("|", end="")
bar_count = 0
for t, m, name in notes_sequence[:80]:
    note_letter = name[:-1]  # strip octave
    # ABC notation: lowercase for higher octave
    octave = int(name[-1])
    if octave >= 5:
        note_letter = note_letter.lower()
    print(f"{note_letter} ", end="")
    bar_count += 1
    if bar_count % 16 == 0:
        print("|")
        if bar_count % 64 == 0:
            print("|", end="")
print("|")


Explanation: Das Skript ist geschrieben. Jetzt führe ich es aus, um die Melodie zu extrahieren.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
=== MELODY EXTRACTION (pYIN) ===
Total voiced frames: 115
Unique note changes: 8
Note range: D2 to G5

=== First 60 note changes ===
    0.42s | MIDI  38 |   D2
    6.87s | MIDI  72 |   C5
   21.46s | MIDI  38 |   D2
   39.43s | MIDI  57 |   A3
   43.10s | MIDI  78 |  F#5
   44.47s | MIDI  79 |   G5
   46.35s | MIDI  78 |  F#5
   46.76s | MIDI  79 |   G5

=== Note distribution (top 15) ===
    D2 (MIDI  38):   88 frames ( 76.5%) ████████████████████████████████████████████████████████████████████████████
    G5 (MIDI  79):   11 frames (  9.6%) █████████
   F#5 (MIDI  78):   11 frames (  9.6%) █████████
    C5 (MIDI  72):    4 frames (  3.5%) ███
    A3 (MIDI  57):    1 frames (  0.9%)

=== ABC Notation (simplified) ===
X:1
T:Yananlar Var
M:4/4
L:1/8
K:D
|D c D A f# g f# g |


Die pYIN-Analyse hat nur 8 Notenwechsel gefunden — der Drohne-Ton (D2) dominiert. Ich muss die Melodie besser isolieren. Neuer Ansatz: Harmonische Separation + höherer Frequenzbereich + anderer Song-Ausschnitt.

Explanation:
The pYIN analysis only found 8 unique note changes in 60 seconds — that's way too sparse. The issue is that this is a vocal piece with instrumental accompaniment, and pYIN is struggling. Let me try a different approach:

1. Use a better pitch detection method (CREPE or just simpler approach)
2. Or try to isolate the vocal melody better
3. Or use a different segment of the song

Actually, looking at the results more carefully: 76.5% of frames are D2 — that's likely the drone/bass note, not the melody. The actual melody notes (C5, F#5, G5) are sparse. The problem is pYIN is picking up the drone instead of the melody.

Let me try:
1. Harmonic-percussive separation to isolate the melodic content
2. Then run pYIN on the harmonic component
3. Or use a higher frequency range for pitch detection

Also, I should try a different segment — maybe the first 60 seconds is mostly instrumental intro. Let me try starting at 30 seconds or so.

Let me write a better script.

import librosa
import numpy as np

# Load a different segment — skip intro, go to vocal part
y_full, sr = librosa.load("/tmp/yananlar_var.wav", sr=22050, duration=120.0, offset=30.0)

# Harmonic-percussive separation — melody is in harmonic part
y_harm, y_perc = librosa.effects.hpss(y_full)

# Pitch tracking on harmonic component, focus on vocal range
f0, voiced_flag, voiced_prob = librosa.pyin(
    y_harm,
    fmin=librosa.note_to_hz('C3'),  # higher min to avoid drone
    fmax=librosa.note_to_hz('C6'),
    sr=sr
)

times = librosa.times_like(f0, sr=sr)
midi_notes = librosa.hz_to_midi(f0)

# Filter to voiced frames with high confidence
confident = voiced_flag & (voiced_prob > 0.7)  # stricter threshold
times_conf = times[confident]
midi_conf = midi_notes[confident]
midi_rounded = np.round(midi_conf).astype(int)

note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']

def midi_to_name(m):
    octave = (m // 12) - 1
    note = note_names[m % 12]
    return f"{note}{octave}"

# Group consecutive same notes
notes_sequence = []
prev_note = None
for t, m in zip(times_conf, midi_rounded):
    name = midi_to_name(m)
    if name != prev_note:
        notes_sequence.append((t, m, name))
        prev_note = name

print(f"=== MELODY EXTRACTION v2 (HPSS + pYIN, offset=30s) ===")
print(f"Total voiced frames: {len(times_conf)}")
print(f"Unique note changes: {len(notes_sequence)}")
if len(midi_rounded) > 0:
    print(f"Note range: {midi_to_name(midi_rounded.min())} to {midi_to_name(midi_rounded.max())}")
print()

# Print all note changes
print("=== All note changes ===")
for i, (t, m, name) in enumerate(notes_sequence):
    print(f"  {t+30:6.2f}s | MIDI {m:3d} | {name:>4}")

# Note distribution
if len(midi_rounded) > 0:
    print(f"\n=== Note distribution ===")
    unique, counts = np.unique(midi_rounded, return_counts=True)
    sorted_idx = np.argsort(counts)[::-1]
    for idx in sorted_idx[:20]:
        name = midi_to_name(unique[idx])
        pct = counts[idx] / len(midi_rounded) * 100
        bar = "█" * int(pct)
        print(f"  {name:>4} (MIDI {unique[idx]:3d}): {counts[idx]:4d} frames ({pct:5.1f}%) {bar}")

# Also try CREPE-style approach: just use spectral peaks
print(f"\n=== Alternative: Spectral-peak based melody ===")
# Compute STFT
S = np.abs(librosa.stft(y_harm))
freqs = librosa.fft_frequencies(sr=sr)
times_spec = librosa.frames_to_time(np.arange(S.shape[1]), sr=sr)

# For each frame, find the strongest peak in vocal range (C3-C6)
c3_bin = np.searchsorted(freqs, librosa.note_to_hz('C3'))
c6_bin = np.searchsorted(freqs, librosa.note_to_hz('C6'))

peak_notes = []
for i in range(S.shape[1]):
    frame = S[c3_bin:c6_bin, i]
    if frame.max() > 0:
        peak_bin = c3_bin + np.argmax(frame)
        peak_hz = freqs[peak_bin]
        peak_midi = librosa.hz_to_midi(peak_hz)
        peak_rounded = int(round(peak_midi))
        peak_notes.append((times_spec[i], peak_rounded, midi_to_name(peak_rounded)))

# Group consecutive same notes
peak_seq = []
prev = None
for t, m, name in peak_notes:
    if name != prev:
        peak_seq.append((t, m, name))
        prev = name

print(f"Total spectral peaks: {len(peak_seq)} note changes")
print("=== First 40 spectral-peak note changes ===")
for i, (t, m, name) in enumerate(peak_seq[:40]):
    print(f"  {t+30:6.2f}s | MIDI {m:3d} | {name:>4}")


Explanation: Let me run the second melodyextraction script.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
=== MELODY EXTRACTION v2 (HPSS + pYIN, offset=30s) ===
Total voiced frames: 15
Unique note changes: 2
Note range: F#5 to G5

=== All note changes ===
   31.95s | MIDI  78 |  F#5
   45.95s | MIDI  79 |   G5

=== Note distribution ===
   F#5 (MIDI  78):   10 frames ( 66.7%) ██████████████████████████████████████████████████████████████████
    G5 (MIDI  79):    5 frames ( 33.3%) █████████████████████████████████

=== Alternative: Spectral-peak based melody ===
Total spectral peaks: 1730 note changes
=== First 40 spectral-peak note changes ===
   30.00s | MIDI  78 |  F#5
   30.02s | MIDI  77 |   F5
   30.07s | MIDI  74 |   D5
   30.09s | MIDI  78 |  F#5
   30.19s | MIDI  77 |   F5
   30.21s | MIDI  76 |   E5
   30.28s | MIDI  74 |   D5
   30.33s | MIDI  78 |  F#5
   30.35s | MIDI  57 |   A3
   30.37s | MIDI  76 |   E5
   30.58s | MIDI  74 |   D5
   30.84s | MIDI  50 |   D3
   30.91s | MIDI  74 |   D5
   30.93s | MIDI  50 |   D3
   31.07s | MIDI  74 |   D5
   31.11s | MIDI  50 |   D3
   31.16s | MIDI  62 |   D4
   31.23s | MIDI  50 |   D3
   31.81s | MIDI  74 |   D5
   31.93s | MIDI  78 |  F#5
   32.16s | MIDI  50 |   D3
   32.18s | MIDI  78 |  F#5
   32.25s | MIDI  74 |   D5
   32.41s | MIDI  50 |   D3
   32.44s | MIDI  78 |  F#5
   32.53s | MIDI  50 |   D3
   32.60s | MIDI  74 |   D5
   32.74s | MIDI  62 |   D4
   32.88s | MIDI  74 |   D5
   33.09s | MIDI  62 |   D4
   33.18s | MIDI  74 |   D5
   33.46s | MIDI  50 |   D3
   33.58s | MIDI  74 |   D5
   33.60s | MIDI  50 |   D3
   33.65s | MIDI  74 |   D5
   33.76s | MIDI  50 |   D3
   33.85s | MIDI  74 |   D5
   34.23s | MIDI  50 |   D3
   34.30s | MIDI  74 |   D5
   34.39s | MIDI  62 |   D4


Die pYIN-Methode findet kaum Noten — der Gesang ist zu dekorativ/melismatisch. Lass mich einen dritten Ansatz mit besserer Glättung und anderem Segment probieren:

Explanation:
Die pYIN-Analyse findet nur sehr wenige Noten (F#5 und G5), weil der Gesang in diesem Lied wahrscheinlich sehr dekorativ/melismatisch ist und pYIN Schwierigkeiten hat. Die Spectral-Peak-Methode findet 1730 Notenwechsel, aber das ist zu granular (jeder Frame ~0.02s).

Ich muss einen besseren Ansatz wählen: Die Melodie dieses Liedes ist wahrscheinlich modal (Makam-basiert) und der Gesang ist nicht stark genug für automatische Extraktion. Ich sollte:

1. Einen anderen Zeitabschnitt wählen, wo der Gesang klarer ist
2. Oder die spektrale Methode mit besserer Glättung verwenden
3. Oder dem Boss ehrlich sagen, dass die automatische Extraktion bei diesem speziellen Lied schwierig ist und ich stattdessen die harmonische Struktur (Akkorde + Tonart) als Grundlage für die Notation gebe

Ich denke, ich sollte einen dritten Versuch mit einem anderen Segment machen (vielleicht ab 60s wo der Gesang stärker ist) und die spektrale Methode mit Median-Filter glätten. Wenn das auch nicht klappt, gebe ich dem Boss die Akkordstruktur und schlage vor, dass er die Melodie nach Gehör ergänzt.

Lass mich einen dritten Versuch machen mit einem anderen Segment und besserer Glättung.

import librosa
import numpy as np
from scipy.ndimage import median_filter

# Try segment 60-120s where vocals might be clearer
y_full, sr = librosa.load("/tmp/yananlar_var.wav", sr=22050, duration=60.0, offset=60.0)

# Harmonic separation
y_harm, y_perc = librosa.effects.hpss(y_full)

# Spectral peak approach with smoothing
S = np.abs(librosa.stft(y_harm, n_fft=4096))
freqs = librosa.fft_frequencies(sr=sr, n_fft=4096)
times_spec = librosa.frames_to_time(np.arange(S.shape[1]), sr=sr, n_fft=4096)

# Focus on vocal range
c3_bin = np.searchsorted(freqs, librosa.note_to_hz('C3'))
c6_bin = np.searchsorted(freqs, librosa.note_to_hz('C6'))

# Find strongest peak per frame
peak_midis = []
for i in range(S.shape[1]):
    frame = S[c3_bin:c6_bin, i]
    if frame.max() > np.median(frame) * 2:  # only if peak is significant
        peak_bin = c3_bin + np.argmax(frame)
        peak_hz = freqs[peak_bin]
        peak_midi = int(round(librosa.hz_to_midi(peak_hz)))
        peak_midis.append(peak_midi)
    else:
        peak_midis.append(-1)  # silence marker

# Smooth with median filter (window ~0.2s = ~9 frames at hop_length=512)
peak_midis = np.array(peak_midis)
smoothed = median_filter(peak_midis, size=9)

# Convert to note names, group consecutive
note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
def midi_to_name(m):
    if m < 0: return "REST"
    octave = (m // 12) - 1
    return f"{note_names[m % 12]}{octave}"

notes_seq = []
prev = None
for i, m in enumerate(smoothed):
    if m < 0:
        name = "REST"
    else:
        name = midi_to_name(m)
    if name != prev:
        notes_seq.append((times_spec[i] + 60, m, name))
        prev = name

print(f"=== MELODY v3 (smoothed spectral peaks, offset=60s) ===")
print(f"Note changes: {len(notes_seq)}")
print()

# Print all note changes
print("=== All note changes ===")
for t, m, name in notes_seq:
    if m >= 0:
        print(f"  {t:6.2f}s | MIDI {m:3d} | {name:>4}")
    else:
        print(f"  {t:6.2f}s | REST")

# Note distribution (excluding rests)
valid = smoothed[smoothed >= 0]
if len(valid) > 0:
    print(f"\n=== Note distribution ===")
    unique, counts = np.unique(valid, return_counts=True)
    sorted_idx = np.argsort(counts)[::-1]
    for idx in sorted_idx[:15]:
        name = midi_to_name(unique[idx])
        pct = counts[idx] / len(valid) * 100
        bar = "█" * int(pct / 2)
        print(f"  {name:>4} (MIDI {unique[idx]:3d}): {counts[idx]:4d} frames ({pct:5.1f}%) {bar}")

# Also try segment 120-180s
print(f"\n\n=== ALSO TRYING SEGMENT 120-180s ===")
y2, _ = librosa.load("/tmp/yananlar_var.wav", sr=22050, duration=60.0, offset=120.0)
y2_harm, _ = librosa.effects.hpss(y2)
S2 = np.abs(librosa.stft(y2_harm, n_fft=4096))
times2 = librosa.frames_to_time(np.arange(S2.shape[1]), sr=sr, n_fft=4096)

peak_midis2 = []
for i in range(S2.shape[1]):
    frame = S2[c3_bin:c6_bin, i]
    if frame.max() > np.median(frame) * 2:
        peak_bin = c3_bin + np.argmax(frame)
        peak_hz = freqs[peak_bin]
        peak_midis2.append(int(round(librosa.hz_to_midi(peak_hz))))
    else:
        peak_midis2.append(-1)

smoothed2 = median_filter(np.array(peak_midis2), size=9)

notes_seq2 = []
prev = None
for i, m in enumerate(smoothed2):
    name = midi_to_name(m) if m >= 0 else "REST"
    if name != prev:
        notes_seq2.append((times2[i] + 120, m, name))
        prev = name

print(f"Note changes: {len(notes_seq2)}")
print("=== All note changes ===")
for t, m, name in notes_seq2:
    if m >= 0:
        print(f"  {t:6.2f}s | MIDI {m:3d} | {name:>4}")
    else:
        print(f"  {t:6.2f}s | REST")


Explanation: The user wants me to extract the melody from the song "Yananlar Var". I've been trying different approaches with librosa. Let me run this third script and see if it gives better results.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
=== MELODY v3 (smoothed spectral peaks, offset=60s) ===
Note changes: 207

=== All note changes ===
   60.09s | MIDI  78 |  F#5
   60.49s | MIDI  79 |   G5
   60.53s | MIDI  78 |  F#5
   60.56s | MIDI  79 |   G5
   60.91s | MIDI  69 |   A4
   61.02s | MIDI  78 |  F#5
   61.14s | MIDI  50 |   D3
   61.18s | MIDI  78 |  F#5
   61.25s | MIDI  77 |   F5
   61.30s | MIDI  78 |  F#5
   61.44s | MIDI  77 |   F5
   61.49s | MIDI  78 |  F#5
   61.51s | MIDI  77 |   F5
   61.65s | MIDI  57 |   A3
   61.70s | MIDI  50 |   D3
   61.83s | MIDI  77 |   F5
   61.86s | MIDI  78 |  F#5
   61.97s | MIDI  74 |   D5
   62.18s | MIDI  50 |   D3
   62.37s | MIDI  54 |  F#3
   62.58s | MIDI  58 |  A#3
   62.60s | MIDI  74 |   D5
   62.67s | MIDI  78 |  F#5
   62.79s | MIDI  50 |   D3
   62.95s | MIDI  57 |   A3
   63.00s | MIDI  60 |   C4
   63.04s | MIDI  64 |   E4
   63.07s | MIDI  60 |   C4
   63.11s | MIDI  59 |   B3
   63.13s | MIDI  58 |  A#3
   63.20s | MIDI  57 |   A3
   63.23s | MIDI  50 |   D3
   63.48s | MIDI  54 |  F#3
   63.67s | MIDI  57 |   A3
   63.72s | MIDI  74 |   D5
   63.78s | MIDI  57 |   A3
   63.90s | MIDI  50 |   D3
   63.95s | MIDI  57 |   A3
   64.02s | MIDI  58 |  A#3
   64.06s | MIDI  70 |  A#4
   64.09s | MIDI  74 |   D5
   64.13s | MIDI  83 |   B5
   64.32s | MIDI  82 |  A#5
   64.37s | MIDI  50 |   D3
   64.50s | MIDI  69 |   A4
   64.53s | MIDI  70 |  A#4
   64.62s | MIDI  69 |   A4
   64.78s | MIDI  70 |  A#4
   64.81s | MIDI  82 |  A#5
   64.90s | MIDI  79 |   G5
   64.97s | MIDI  50 |   D3
   65.04s | MIDI  76 |   E5
   65.29s | MIDI  77 |   F5
   65.48s | MIDI  50 |   D3
   65.62s | MIDI  51 |  D#3
   65.64s | MIDI  79 |   G5
   65.76s | MIDI  69 |   A4
   65.83s | MIDI  53 |   F3
   65.87s | MIDI  69 |   A4
   65.97s | MIDI  50 |   D3
   66.04s | MIDI  77 |   F5
   66.06s | MIDI  50 |   D3
   66.29s | MIDI  78 |  F#5
   66.46s | MIDI  77 |   F5
   66.48s | MIDI  50 |   D3
   66.99s | MIDI  49 |  C#3
   67.06s | MIDI  50 |   D3
   67.38s | MIDI  71 |   B4
   67.41s | MIDI  72 |   C5
   67.59s | MIDI  50 |   D3
   67.83s | MIDI  51 |  D#3
   67.85s | MIDI  53 |   F3
   68.01s | MIDI  63 |  D#4
   68.03s | MIDI  72 |   C5
   68.17s | MIDI  63 |  D#4
   68.20s | MIDI  50 |   D3
   68.36s | MIDI  72 |   C5
   68.57s | MIDI  71 |   B4
   68.66s | MIDI  69 |   A4
   68.80s | MIDI  50 |   D3
   68.94s | MIDI  69 |   A4
   69.01s | MIDI  71 |   B4
   69.13s | MIDI  69 |   A4
   69.36s | MIDI  50 |   D3
   69.52s | MIDI  67 |   G4
   69.82s | MIDI  69 |   A4
   70.10s | MIDI  51 |  D#3
   70.12s | MIDI  50 |   D3
   70.26s | MIDI  52 |   E3
   70.38s | MIDI  50 |   D3
   70.57s | MIDI  66 |  F#4
   70.77s | MIDI  62 |   D4
   70.82s | MIDI  53 |   F3
   70.84s | MIDI  50 |   D3
   71.12s | MIDI  54 |  F#3
   71.33s | MIDI  58 |  A#3
   71.35s | MIDI  69 |   A4
   71.45s | MIDI  58 |  A#3
   71.47s | MIDI  50 |   D3
   71.66s | MIDI  51 |  D#3
   71.68s | MIDI  52 |   E3
   71.73s | MIDI  53 |   F3
   71.75s | MIDI  58 |  A#3
   71.77s | MIDI  76 |   E5
   71.80s | MIDI  58 |  A#3
   71.96s | MIDI  57 |   A3
   71.98s | MIDI  50 |   D3
   72.24s | MIDI  53 |   F3
   72.28s | MIDI  70 |  A#4
   72.38s | MIDI  62 |   D4
   72.54s | MIDI  50 |   D3
   72.79s | MIDI  57 |   A3
   73.12s | MIDI  50 |   D3
   73.37s | MIDI  52 |   E3
   73.42s | MIDI  57 |   A3
   73.68s | MIDI  50 |   D3
   73.86s | MIDI  57 |   A3
   73.91s | MIDI  62 |   D4
   73.93s | MIDI  69 |   A4
   74.19s | MIDI  50 |   D3
   74.42s | MIDI  57 |   A3
   74.47s | MIDI  74 |   D5
   74.58s | MIDI  72 |   C5
   74.88s | MIDI  83 |   B5
   74.91s | MIDI  84 |   C6
   75.14s | MIDI  83 |   B5
   75.74s | MIDI  81 |   A5
   75.88s | MIDI  79 |   G5
   76.18s | MIDI  81 |   A5
   76.30s | MIDI  83 |   B5
   76.86s | MIDI  81 |   A5
   77.37s | MIDI  79 |   G5
   77.51s | MIDI  50 |   D3
   77.65s | MIDI  54 |  F#3
   77.86s | MIDI  53 |   F3
   77.95s | MIDI  54 |  F#3
   77.97s | MIDI  53 |   F3
   78.00s | MIDI  52 |   E3
   78.30s | MIDI  69 |   A4
   78.34s | MIDI  79 |   G5
   78.58s | MIDI  81 |   A5
   79.02s | MIDI  79 |   G5
   79.04s | MIDI  69 |   A4
   79.25s | MIDI  79 |   G5
   79.32s | MIDI  69 |   A4
   79.37s | MIDI  79 |   G5
   79.48s | MIDI  69 |   A4
   79.53s | MIDI  67 |   G4
   79.60s | MIDI  54 |  F#3
   79.69s | MIDI  50 |   D3
   79.85s | MIDI  54 |  F#3
   79.88s | MIDI  78 |  F#5
   80.06s | MIDI  76 |   E5
   80.25s | MIDI  62 |   D4
   80.39s | MIDI  57 |   A3
   80.43s | MIDI  52 |   E3
   80.71s | MIDI  57 |   A3
   80.99s | MIDI  74 |   D5
   81.06s | MIDI  76 |   E5
   81.27s | MIDI  66 |  F#4
   81.29s | MIDI  57 |   A3
   81.39s | MIDI  66 |  F#4
   81.52s | MIDI  54 |  F#3
   81.78s | MIDI  53 |   F3
   81.80s | MIDI  64 |   E4
   81.85s | MIDI  76 |   E5
   82.29s | MIDI  61 |  C#4
   82.31s | MIDI  50 |   D3
   82.66s | MIDI  69 |   A4
   82.96s | MIDI  50 |   D3
   83.15s | MIDI  54 |  F#3
   83.36s | MIDI  72 |   C5
   83.57s | MIDI  60 |   C4
   83.71s | MIDI  72 |   C5
   83.99s | MIDI  83 |   B5
   84.43s | MIDI  72 |   C5
   84.47s | MIDI  57 |   A3
   84.57s | MIDI  50 |   D3
   84.61s | MIDI  57 |   A3
   84.66s | MIDI  67 |   G4
   84.82s | MIDI  69 |   A4
   84.94s | MIDI  81 |   A5
   85.01s | MIDI  83 |   B5
   85.52s | MIDI  81 |   A5
   85.61s | MIDI  69 |   A4
   85.63s | MIDI  81 |   A5
   85.87s | MIDI  69 |   A4
   86.31s | MIDI  78 |  F#5
   86.49s | MIDI  55 |   G3
   86.82s | MIDI  57 |   A3
   86.94s | MIDI  62 |   D4
   87.00s | MIDI  67 |   G4
   87.03s | MIDI  79 |   G5
   87.12s | MIDI  81 |   A5
   87.42s | MIDI  69 |   A4
   87.89s | MIDI  79 |   G5
   88.07s | MIDI  69 |   A4
   88.17s | MIDI  67 |   G4
   88.26s | MIDI  69 |   A4
   88.31s | MIDI  78 |  F#5
   88.77s | MIDI  52 |   E3
   88.86s | MIDI  50 |   D3
   89.21s | MIDI  57 |   A3
   89.42s | MIDI  50 |   D3
   89.68s | MIDI  51 |  D#3
   89.70s | MIDI  76 |   E5
   89.98s | MIDI  50 |   D3

=== Note distribution ===
    D3 (MIDI  50):  284 frames ( 22.0%) ██████████
    A4 (MIDI  69):  145 frames ( 11.2%) █████
    B5 (MIDI  83):  100 frames (  7.7%) ███
   F#5 (MIDI  78):   86 frames (  6.7%) ███
    A3 (MIDI  57):   83 frames (  6.4%) ███
    A5 (MIDI  81):   82 frames (  6.3%) ███
    G5 (MIDI  79):   75 frames (  5.8%) ██
   F#3 (MIDI  54):   61 frames (  4.7%) ██
    E5 (MIDI  76):   60 frames (  4.6%) ██
    C5 (MIDI  72):   59 frames (  4.6%) ██
    E3 (MIDI  52):   38 frames (  2.9%) █
    G4 (MIDI  67):   28 frames (  2.2%) █
    D5 (MIDI  74):   25 frames (  1.9%)
    F5 (MIDI  77):   21 frames (  1.6%)
    D4 (MIDI  62):   19 frames (  1.5%)

=== ALSO TRYING SEGMENT 120-180s ===
Note changes: 249
=== All note changes ===
  120.09s | MIDI  67 |   G4
  120.19s | MIDI  54 |  F#3
  120.39s | MIDI  57 |   A3
  120.46s | MIDI  53 |   F3
  120.49s | MIDI  52 |   E3
  120.51s | MIDI  50 |   D3
  120.65s | MIDI  52 |   E3
  120.70s | MIDI  76 |   E5
  120.79s | MIDI  57 |   A3
  120.98s | MIDI  52 |   E3
  121.07s | MIDI  50 |   D3
  121.23s | MIDI  51 |  D#3
  121.25s | MIDI  52 |   E3
  121.30s | MIDI  53 |   F3
  121.37s | MIDI  57 |   A3
  121.42s | MIDI  58 |  A#3
  121.49s | MIDI  62 |   D4
  121.90s | MIDI  74 |   D5
  121.97s | MIDI  69 |   A4
  122.04s | MIDI  62 |   D4
  122.09s | MIDI  69 |   A4
  122.14s | MIDI  64 |   E4
  122.55s | MIDI  62 |   D4
  122.60s | MIDI  57 |   A3
  122.62s | MIDI  51 |  D#3
  122.65s | MIDI  50 |   D3
  122.86s | MIDI  54 |  F#3
  123.02s | MIDI  72 |   C5
  123.11s | MIDI  53 |   F3
  123.13s | MIDI  52 |   E3
  123.46s | MIDI  53 |   F3
  123.51s | MIDI  60 |   C4
  123.53s | MIDI  67 |   G4
  123.95s | MIDI  55 |   G3
  124.09s | MIDI  73 |  C#5
  124.13s | MIDI  74 |   D5
  124.20s | MIDI  73 |  C#5
  124.30s | MIDI  66 |  F#4
  124.44s | MIDI  74 |   D5
  124.53s | MIDI  72 |   C5
  124.78s | MIDI  64 |   E4
  124.92s | MIDI  63 |  D#4
  124.95s | MIDI  49 |  C#3
  125.09s | MIDI  52 |   E3
  125.39s | MIDI  57 |   A3
  125.57s | MIDI  50 |   D3
  125.74s | MIDI  53 |   F3
  125.85s | MIDI  50 |   D3
  126.06s | MIDI  62 |   D4
  126.13s | MIDI  69 |   A4
  126.59s | MIDI  64 |   E4
  126.94s | MIDI  57 |   A3
  126.99s | MIDI  53 |   F3
  127.01s | MIDI  50 |   D3
  127.24s | MIDI  51 |  D#3
  127.27s | MIDI  54 |  F#3
  127.29s | MIDI  55 |   G3
  127.31s | MIDI  66 |  F#4
  127.36s | MIDI  73 |  C#5
  127.43s | MIDI  74 |   D5
  127.48s | MIDI  73 |  C#5
  127.55s | MIDI  54 |  F#3
  127.59s | MIDI  52 |   E3
  127.85s | MIDI  53 |   F3
  127.92s | MIDI  54 |  F#3
  127.94s | MIDI  67 |   G4
  128.06s | MIDI  69 |   A4
  128.24s | MIDI  76 |   E5
  128.52s | MIDI  69 |   A4
  128.59s | MIDI  76 |   E5
  129.03s | MIDI  69 |   A4
  129.10s | MIDI  76 |   E5
  129.13s | MIDI  69 |   A4
  129.15s | MIDI  76 |   E5
  129.22s | MIDI  81 |   A5
  129.26s | MIDI  76 |   E5
  129.40s | MIDI  69 |   A4
  130.19s | MIDI  68 |  G#4
  130.24s | MIDI  74 |   D5
  130.31s | MIDI  58 |  A#3
  130.40s | MIDI  50 |   D3
  130.50s | MIDI  62 |   D4
  130.63s | MIDI  74 |   D5
  130.77s | MIDI  73 |  C#5
  130.80s | MIDI  72 |   C5
  130.98s | MIDI  79 |   G5
  131.15s | MIDI  78 |  F#5
  131.17s | MIDI  73 |  C#5
  131.19s | MIDI  72 |   C5
  131.38s | MIDI  71 |   B4
  131.56s | MIDI  72 |   C5
  131.63s | MIDI  71 |   B4
  131.96s | MIDI  61 |  C#4
  132.00s | MIDI  58 |  A#3
  132.14s | MIDI  55 |   G3
  132.26s | MIDI  52 |   E3
  132.47s | MIDI  59 |   B3
  132.49s | MIDI  60 |   C4
  132.56s | MIDI  71 |   B4
  132.63s | MIDI  72 |   C5
  132.70s | MIDI  78 |  F#5
  132.72s | MIDI  79 |   G5
  132.77s | MIDI  83 |   B5
  132.89s | MIDI  79 |   G5
  132.91s | MIDI  72 |   C5
  132.98s | MIDI  71 |   B4
  133.14s | MIDI  70 |  A#4
  133.21s | MIDI  69 |   A4
  133.35s | MIDI  76 |   E5
  133.47s | MIDI  71 |   B4
  133.54s | MIDI  76 |   E5
  133.58s | MIDI  71 |   B4
  133.65s | MIDI  55 |   G3
  133.72s | MIDI  50 |   D3
  133.86s | MIDI  53 |   F3
  134.00s | MIDI  54 |  F#3
  134.07s | MIDI  53 |   F3
  134.14s | MIDI  54 |  F#3
  134.16s | MIDI  53 |   F3
  134.33s | MIDI  50 |   D3
  134.47s | MIDI  53 |   F3
  134.49s | MIDI  54 |  F#3
  134.54s | MIDI  62 |   D4
  134.58s | MIDI  65 |   F4
  134.61s | MIDI  69 |   A4
  135.09s | MIDI  76 |   E5
  135.26s | MIDI  67 |   G4
  135.28s | MIDI  55 |   G3
  135.39s | MIDI  73 |  C#5
  135.42s | MIDI  74 |   D5
  135.56s | MIDI  73 |  C#5
  135.60s | MIDI  74 |   D5
  135.65s | MIDI  69 |   A4
  135.72s | MIDI  68 |  G#4
  135.79s | MIDI  66 |  F#4
  135.93s | MIDI  50 |   D3
  136.09s | MIDI  55 |   G3
  136.16s | MIDI  66 |  F#4
  136.18s | MIDI  67 |   G4
  136.42s | MIDI  66 |  F#4
  136.46s | MIDI  52 |   E3
  136.70s | MIDI  53 |   F3
  136.79s | MIDI  62 |   D4
  136.81s | MIDI  64 |   E4
  137.00s | MIDI  52 |   E3
  137.62s | MIDI  64 |   E4
  137.65s | MIDI  67 |   G4
  137.97s | MIDI  65 |   F4
  138.00s | MIDI  64 |   E4
  138.20s | MIDI  71 |   B4
  138.27s | MIDI  54 |  F#3
  138.34s | MIDI  71 |   B4
  138.41s | MIDI  54 |  F#3
  138.46s | MIDI  71 |   B4
  138.69s | MIDI  54 |  F#3
  138.72s | MIDI  50 |   D3
  138.90s | MIDI  52 |   E3
  138.92s | MIDI  57 |   A3
  139.09s | MIDI  71 |   B4
  139.20s | MIDI  62 |   D4
  139.23s | MIDI  50 |   D3
  139.34s | MIDI  62 |   D4
  139.48s | MIDI  74 |   D5
  139.69s | MIDI  72 |   C5
  139.81s | MIDI  74 |   D5
  139.83s | MIDI  78 |  F#5
  139.88s | MIDI  79 |   G5
  139.97s | MIDI  78 |  F#5
  139.99s | MIDI  74 |   D5
  140.11s | MIDI  72 |   C5
  140.18s | MIDI  71 |   B4
  140.39s | MIDI  72 |   C5
  140.53s | MIDI  71 |   B4
  140.71s | MIDI  60 |   C4
  140.76s | MIDI  59 |   B3
  140.81s | MIDI  60 |   C4
  140.83s | MIDI  59 |   B3
  140.87s | MIDI  57 |   A3
  140.92s | MIDI  52 |   E3
  141.04s | MIDI  55 |   G3
  141.13s | MIDI  69 |   A4
  141.29s | MIDI  71 |   B4
  141.50s | MIDI  78 |  F#5
  141.52s | MIDI  72 |   C5
  141.69s | MIDI  78 |  F#5
  141.71s | MIDI  72 |   C5
  141.78s | MIDI  71 |   B4
  141.94s | MIDI  76 |   E5
  142.15s | MIDI  69 |   A4
  142.45s | MIDI  53 |   F3
  142.59s | MIDI  54 |  F#3
  142.83s | MIDI  72 |   C5
  142.85s | MIDI  73 |  C#5
  143.03s | MIDI  72 |   C5
  143.06s | MIDI  50 |   D3
  143.48s | MIDI  69 |   A4
  143.61s | MIDI  49 |  C#3
  143.96s | MIDI  58 |  A#3
  144.08s | MIDI  57 |   A3
  144.17s | MIDI  54 |  F#3
  144.29s | MIDI  69 |   A4
  144.31s | MIDI  54 |  F#3
  144.36s | MIDI  69 |   A4
  144.38s | MIDI  74 |   D5
  144.57s | MIDI  66 |  F#4
  144.66s | MIDI  54 |  F#3
  144.71s | MIDI  66 |  F#4
  144.87s | MIDI  67 |   G4
  144.92s | MIDI  66 |  F#4
  144.98s | MIDI  67 |   G4
  145.17s | MIDI  66 |  F#4
  145.22s | MIDI  53 |   F3
  145.24s | MIDI  54 |  F#3
  145.29s | MIDI  53 |   F3
  145.38s | MIDI  54 |  F#3
  145.47s | MIDI  53 |   F3
  145.54s | MIDI  50 |   D3
  145.80s | MIDI  62 |   D4
  146.10s | MIDI  71 |   B4
  146.28s | MIDI  64 |   E4
  146.31s | MIDI  63 |  D#4
  146.33s | MIDI  50 |   D3
  146.49s | MIDI  53 |   F3
  146.56s | MIDI  54 |  F#3
  146.66s | MIDI  64 |   E4
  146.89s | MIDI  50 |   D3
  147.12s | MIDI  52 |   E3
  147.14s | MIDI  67 |   G4
  147.19s | MIDI  57 |   A3
  147.24s | MIDI  48 |   C3
  147.45s | MIDI  50 |   D3
  147.61s | MIDI  53 |   F3
  147.65s | MIDI  57 |   A3
  147.77s | MIDI  69 |   A4
  147.84s | MIDI  74 |   D5
  147.96s | MIDI  50 |   D3
  148.24s | MIDI  74 |   D5
  148.26s | MIDI  54 |  F#3
  148.28s | MIDI  72 |   C5
  148.42s | MIDI  57 |   A3
  148.47s | MIDI  72 |   C5
  148.82s | MIDI  58 |  A#3
  148.96s | MIDI  72 |   C5
  149.05s | MIDI  83 |   B5
  149.51s | MIDI  81 |   A5
  149.56s | MIDI  57 |   A3
  149.61s | MIDI  55 |   G3
  149.86s | MIDI  69 |   A4
  150.02s | MIDI  83 |   B5


Gute Daten! Jetzt mache ich daraus eine saubere ABC-Notation und rendere sie als Notenbild:

Explanation:
Die Melodie-Extraktion hat jetzt gute Daten geliefert. Ich habe zwei Segmente analysiert (60-120s und 120-180s) mit vielen Notenwechseln. Jetzt muss ich das in eine brauchbare Form bringen.

Die Rohdaten sind zu granular (viele schnelle Wechsel durch Melismen/Ornamente). Ich sollte die Melodie in eine vereinfachte, spielbare Form bringen — die Hauptnoten pro Phrase, nicht jedes Mikro-Ornament.

Ich werde:
1. Die Daten in ein Python-Script packen, das die Melodie in sinnvolle Phrasen gruppiert
2. ABC-Notation erstellen
3. Eine visuelle Darstellung (Notenbild) generieren lassen

Lass mich die Melodie in ABC-Notation mit LilyPond oder abcm2ps rendern. Oder einfacher: Ich erstelle eine saubere ABC-Datei und erkläre Boss, wie er sie in Noten umwandeln kann.

Die Melodie aus Segment 60-120s zeigt klare Phrasen:
- Eröffnung: F#5 → G5 → F#5 → G5 (Wechsel)
- Dann A4 → F#5 → D3 (Bass) → F#5 → F5 → F#5
- Dann A3 → D3 → F#5 → D5 → D3 → F#3 → A#3 → D5 → F#5
- Dann D3 → A3 → C4 → E4 → C4 → B3 → A#3 → A3 → D3
- Dann F#3 → A3 → D5 → A3 → D3 → A3 → A#3 → A#4 → D5 → B5 → A#5
- Dann D3 → A4 → A#4 → A4 → A#4 → A#5 → G5 → D3 → E5 → F5
- Dann D3 → D#3 → G5 → A4 → F3 → A4 → D3 → F5 → D3 → F#5 → F5 → D3
- Dann C#3 → D3 → B4 → C5 → D3 → D#3 → F3 → D#4 → C5 → D#4 → D3 → C5 → B4 → A4
- Dann D3 → A4 → B4 → A4 → D3 → G4 → A4 → D#3 → D3 → E3 → D3 → F#4 → D4 → F3 → D3
- Dann F#3 → A#3 → A4 → A#3 → D3 → D#3 → E3 → F3 → A#3 → E5 → A#3 → A3 → D3
- Dann F3 → A#4 → D4 → D3 → A3 → D3 → E3 → A3 → D3 → A3 → D4 → A4 → D3
- Dann A3 → D5 → C5 → B5 → C6 → B5 → A5 → G5 → A5 → B5 → A5 → G5
- Dann D3 → F#3 → F3 → F#3 → F3 → E3 → A4 → G5 → A5 → G5 → A4 → G5 → A4 → G5 → A4 → G4 → F#3 → D3
- Dann F#3 → F#5 → E5 → D4 → A3 → E3 → A3 → D5 → E5 → F#4 → A3 → F#4 → F#3 → F3 → E4 → E5
- Dann C#4 → D3 → A4 → D3 → F#3 → C5 → C4 → C5 → B5 → C5 → A3 → D3 → A3 → G4 → A4 → A5 → B5 → A5 → A4 → A5 → A4
- Dann F#5 → G3 → A3 → D4 → G4 → G5 → A5 → A4 → G5 → A4 → G4 → A4 → F#5 → E3 → D3
- Dann A3 → D3 → D#3 → E5 → D3

Das ist sehr detailliert. Ich sollte die Hauptnoten pro ~2-Sekunden-Phrase extrahieren und in ABC-Notation packen. Dann kann ich mit abcm2ps oder LilyPond ein PDF/PNG generieren.

Lass mich das in einem finalen Script machen, das:
1. Die Melodie in Phrasen gruppiert (alle ~2 Sekunden die dominanteste Note)
2. ABC-Notation erstellt
3. Mit abcm2ps ein PNG generiert (falls installiert)

Oder ich erstelle direkt eine schöne textuelle Darstellung mit den Hauptnoten und ABC-Notation, die Boss dann in jeden ABC-Viewer werfen kann.

import librosa
import numpy as np
from scipy.ndimage import median_filter
from collections import Counter

# Load full song, analyze in 30s chunks
y_full, sr = librosa.load("/tmp/yananlar_var.wav", sr=22050)
y_harm, _ = librosa.effects.hpss(y_full)

# Spectral peak melody extraction
S = np.abs(librosa.stft(y_harm, n_fft=4096, hop_length=512))
freqs = librosa.fft_frequencies(sr=sr, n_fft=4096)
times_spec = librosa.frames_to_time(np.arange(S.shape[1]), sr=sr, n_fft=4096, hop_length=512)

c3_bin = np.searchsorted(freqs, librosa.note_to_hz('C3'))
c6_bin = np.searchsorted(freqs, librosa.note_to_hz('C6'))

peak_midis = []
for i in range(S.shape[1]):
    frame = S[c3_bin:c6_bin, i]
    if frame.max() > np.median(frame) * 2:
        peak_bin = c3_bin + np.argmax(frame)
        peak_hz = freqs[peak_bin]
        peak_midis.append(int(round(librosa.hz_to_midi(peak_hz))))
    else:
        peak_midis.append(-1)

# Smooth
smoothed = median_filter(np.array(peak_midis), size=11)

note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
def midi_to_abc(m):
    """Convert MIDI to ABC notation note"""
    if m < 0: return "z"  # rest
    note = note_names[m % 12]
    octave = (m // 12) - 1
    # ABC: C is middle C (MIDI 60). Below = uppercase with commas, above = lowercase
    if octave < 4:
        return note + "," * (4 - octave)
    elif octave == 4:
        return note
    else:
        return note.lower() + "'" * (octave - 5)

# Group into phrases (~2 second windows) and pick dominant note
phrase_dur = 2.0  # seconds per phrase
total_dur = len(y_full) / sr
num_phrases = int(total_dur / phrase_dur)

print(f"Total duration: {total_dur:.0f}s, Phrases: {num_phrases}")
print()

# Build phrase-by-phrase melody
melody_phrases = []
for p in range(num_phrases):
    start_t = p * phrase_dur
    end_t = (p + 1) * phrase_dur
    mask = (times_spec >= start_t) & (times_spec < end_t)
    notes_in_phrase = smoothed[mask]
    valid = notes_in_phrase[notes_in_phrase >= 0]

    if len(valid) == 0:
        melody_phrases.append(("z", -1, start_t))
        continue

    # Get most common note (mode)
    counter = Counter(valid)
    dominant_midi = counter.most_common(1)[0][0]
    dominant_abc = midi_to_abc(dominant_midi)
    melody_phrases.append((dominant_abc, dominant_midi, start_t))

# Print phrase-by-phrase
print("=== PHRASE-BY-PHRASE MELODY (2s windows) ===")
for abc, midi, t in melody_phrases:
    if midi >= 0:
        name = f"{note_names[midi % 12]}{(midi // 12) - 1}"
        print(f"  {t:6.1f}s | {abc:>6} | {name:>4} (MIDI {midi})")
    else:
        print(f"  {t:6.1f}s |   REST")

# Build ABC notation
print()
print("=== ABC NOTATION ===")
print("X:1")
print("T:Yananlar Var")
print("C:Grup Dergah")
print("M:4/4")
print("L:1/4")
print("Q:1/4=100")
print("K:D")
print("% Melody extracted via spectral analysis")
print("% Each note = dominant pitch in 2-second window")
print()

# Group into bars (4 phrases per bar in 4/4 at L:1/4)
bar_len = 4
for i in range(0, len(melody_phrases), bar_len * 4):  # 4 bars per line
    line_phrases = melody_phrases[i:i + bar_len * 4]
    if not line_phrases:
        break

    # Build bars
    bars = []
    for j in range(0, len(line_phrases), bar_len):
        bar_notes = [p[0] for p in line_phrases[j:j+bar_len]]
        bars.append(" ".join(bar_notes))

    print("| " + " | ".join(bars) + " |")

# Also output as simple note names for easy reading
print()
print("=== SIMPLE NOTE SEQUENCE (for Ney/Saz) ===")
print("Key: D")
print()
prev = None
for abc, midi, t in melody_phrases:
    if midi >= 0:
        name = f"{note_names[midi % 12]}"
        if name != prev:
            print(f"  {name}", end="")
            prev = name
    else:
        if prev != "REST":
            print("  (rest)", end="")
            prev = "REST"
print()


Explanation: The script is written. Let me run it now.


Output:
/DATA/.local/lib/python3.12/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)
Total duration: 383s, Phrases: 191

=== PHRASE-BY-PHRASE MELODY (2s windows) ===
     0.0s |     D, |   D3 (MIDI 50)
     2.0s |     D, |   D3 (MIDI 50)
     4.0s |     A, |   A3 (MIDI 57)
     6.0s |      c |   C5 (MIDI 72)
     8.0s |     D, |   D3 (MIDI 50)
    10.0s |      A |   A4 (MIDI 69)
    12.0s |      A |   A4 (MIDI 69)
    14.0s |      e |   E5 (MIDI 76)
    16.0s |      C |   C4 (MIDI 60)
    18.0s |     D, |   D3 (MIDI 50)
    20.0s |      A |   A4 (MIDI 69)
    22.0s |      A |   A4 (MIDI 69)
    24.0s |      A |   A4 (MIDI 69)
    26.0s |     D, |   D3 (MIDI 50)
    28.0s |     D, |   D3 (MIDI 50)
    30.0s |     D, |   D3 (MIDI 50)
    32.0s |      d |   D5 (MIDI 74)
    34.0s |      d |   D5 (MIDI 74)
    36.0s |     D, |   D3 (MIDI 50)
    38.0s |      A |   A4 (MIDI 69)
    40.0s |      e |   E5 (MIDI 76)
    42.0s |     f# |  F#5 (MIDI 78)
    44.0s |     f# |  F#5 (MIDI 78)
    46.0s |      g |   G5 (MIDI 79)
    48.0s |     A, |   A3 (MIDI 57)
    50.0s |      f |   F5 (MIDI 77)
    52.0s |     D, |   D3 (MIDI 50)
    54.0s |     D, |   D3 (MIDI 50)
    56.0s |     D, |   D3 (MIDI 50)
    58.0s |      e |   E5 (MIDI 76)
    60.0s |     f# |  F#5 (MIDI 78)
    62.0s |     f# |  F#5 (MIDI 78)
    64.0s |     D, |   D3 (MIDI 50)
    66.0s |     D, |   D3 (MIDI 50)
    68.0s |      b |   B5 (MIDI 83)
    70.0s |      f |   F5 (MIDI 77)
    72.0s |     D, |   D3 (MIDI 50)
    74.0s |     D, |   D3 (MIDI 50)
    76.0s |      c |   C5 (MIDI 72)
    78.0s |      A |   A4 (MIDI 69)
    80.0s |     D, |   D3 (MIDI 50)
    82.0s |     D, |   D3 (MIDI 50)
    84.0s |     D, |   D3 (MIDI 50)
    86.0s |     D, |   D3 (MIDI 50)
    88.0s |      c |   C5 (MIDI 72)
    90.0s |      b |   B5 (MIDI 83)
    92.0s |      b |   B5 (MIDI 83)
    94.0s |      a |   A5 (MIDI 81)
    96.0s |      a |   A5 (MIDI 81)
    98.0s |      g |   G5 (MIDI 79)
   100.0s |     E, |   E3 (MIDI 52)
   102.0s |      e |   E5 (MIDI 76)
   104.0s |     D, |   D3 (MIDI 50)
   106.0s |      c |   C5 (MIDI 72)
   108.0s |      b |   B5 (MIDI 83)
   110.0s |      b |   B5 (MIDI 83)
   112.0s |      A |   A4 (MIDI 69)
   114.0s |      A |   A4 (MIDI 69)
   116.0s |     f# |  F#5 (MIDI 78)
   118.0s |     D, |   D3 (MIDI 50)
   120.0s |     D, |   D3 (MIDI 50)
   122.0s |      D |   D4 (MIDI 62)
   124.0s |      E |   E4 (MIDI 64)
   126.0s |      G |   G4 (MIDI 67)
   128.0s |      d |   D5 (MIDI 74)
   130.0s |     D, |   D3 (MIDI 50)
   132.0s |      E |   E4 (MIDI 64)
   134.0s |     D, |   D3 (MIDI 50)
   136.0s |      e |   E5 (MIDI 76)
   138.0s |      A |   A4 (MIDI 69)
   140.0s |      c |   C5 (MIDI 72)
   142.0s |      B |   B4 (MIDI 71)
   144.0s |     E, |   E3 (MIDI 52)
   146.0s |      e |   E5 (MIDI 76)
   148.0s |      A |   A4 (MIDI 69)
   150.0s |      d |   D5 (MIDI 74)
   152.0s |     E, |   E3 (MIDI 52)
   154.0s |     E, |   E3 (MIDI 52)
   156.0s |      B |   B4 (MIDI 71)
   158.0s |      D |   D4 (MIDI 62)
   160.0s |      B |   B4 (MIDI 71)
   162.0s |      B |   B4 (MIDI 71)
   164.0s |      A |   A4 (MIDI 69)
   166.0s |     D, |   D3 (MIDI 50)
   168.0s |     F# |  F#4 (MIDI 66)
   170.0s |     D, |   D3 (MIDI 50)
   172.0s |     D, |   D3 (MIDI 50)
   174.0s |     D, |   D3 (MIDI 50)
   176.0s |      c |   C5 (MIDI 72)
   178.0s |      b |   B5 (MIDI 83)
   180.0s |      b |   B5 (MIDI 83)
   182.0s |     D, |   D3 (MIDI 50)
   184.0s |    C#, |  C#3 (MIDI 49)
   186.0s |      g |   G5 (MIDI 79)
   188.0s |      d |   D5 (MIDI 74)
   190.0s |     E, |   E3 (MIDI 52)
   192.0s |     D, |   D3 (MIDI 50)
   194.0s |     F# |  F#4 (MIDI 66)
   196.0s |      G |   G4 (MIDI 67)
   198.0s |      G |   G4 (MIDI 67)
   200.0s |     D, |   D3 (MIDI 50)
   202.0s |      E |   E4 (MIDI 64)
   204.0s |      G |   G4 (MIDI 67)
   206.0s |      A |   A4 (MIDI 69)
   208.0s |      A |   A4 (MIDI 69)
   210.0s |      d |   D5 (MIDI 74)
   212.0s |      B |   B4 (MIDI 71)
   214.0s |      B |   B4 (MIDI 71)
   216.0s |     F, |   F3 (MIDI 53)
   218.0s |     D, |   D3 (MIDI 50)
   220.0s |      G |   G4 (MIDI 67)
   222.0s |      d |   D5 (MIDI 74)
   224.0s |    F#, |  F#3 (MIDI 54)
   226.0s |      d |   D5 (MIDI 74)
   228.0s |      C |   C4 (MIDI 60)
   230.0s |      B |   B4 (MIDI 71)
   232.0s |      B |   B4 (MIDI 71)
   234.0s |    F#, |  F#3 (MIDI 54)
   236.0s |      G |   G4 (MIDI 67)
   238.0s |      G |   G4 (MIDI 67)
   240.0s |     D, |   D3 (MIDI 50)
   242.0s |     A, |   A3 (MIDI 57)
   244.0s |     D, |   D3 (MIDI 50)
   246.0s |      c |   C5 (MIDI 72)
   248.0s |      c |   C5 (MIDI 72)
   250.0s |      a |   A5 (MIDI 81)
   252.0s |     D, |   D3 (MIDI 50)
   254.0s |      g |   G5 (MIDI 79)
   256.0s |     D, |   D3 (MIDI 50)
   258.0s |      e |   E5 (MIDI 76)
   260.0s |      d |   D5 (MIDI 74)
   262.0s |     D, |   D3 (MIDI 50)
   264.0s |     D, |   D3 (MIDI 50)
   266.0s |      G |   G4 (MIDI 67)
   268.0s |     D, |   D3 (MIDI 50)
   270.0s |     A, |   A3 (MIDI 57)
   272.0s |    F#, |  F#3 (MIDI 54)
   274.0s |     E, |   E3 (MIDI 52)
   276.0s |      A |   A4 (MIDI 69)
   278.0s |     A, |   A3 (MIDI 57)
   280.0s |      c |   C5 (MIDI 72)
   282.0s |     E, |   E3 (MIDI 52)
   284.0s |     E, |   E3 (MIDI 52)
   286.0s |     D, |   D3 (MIDI 50)
   288.0s |      G |   G4 (MIDI 67)
   290.0s |      G |   G4 (MIDI 67)
   292.0s |     D, |   D3 (MIDI 50)
   294.0s |     D, |   D3 (MIDI 50)
   296.0s |     D, |   D3 (MIDI 50)
   298.0s |      B |   B4 (MIDI 71)
   300.0s |      B |   B4 (MIDI 71)
   302.0s |     G, |   G3 (MIDI 55)
   304.0s |     D, |   D3 (MIDI 50)
   306.0s |      G |   G4 (MIDI 67)
   308.0s |     A, |   A3 (MIDI 57)
   310.0s |     E, |   E3 (MIDI 52)
   312.0s |     D, |   D3 (MIDI 50)
   314.0s |      c |   C5 (MIDI 72)
   316.0s |      B |   B4 (MIDI 71)
   318.0s |      A |   A4 (MIDI 69)
   320.0s |     D, |   D3 (MIDI 50)
   322.0s |      A |   A4 (MIDI 69)
   324.0s |     A, |   A3 (MIDI 57)
   326.0s |     E, |   E3 (MIDI 52)
   328.0s |     D, |   D3 (MIDI 50)
   330.0s |      D |   D4 (MIDI 62)
   332.0s |     A, |   A3 (MIDI 57)
   334.0s |      G |   G4 (MIDI 67)
   336.0s |     A, |   A3 (MIDI 57)
   338.0s |     F, |   F3 (MIDI 53)
   340.0s |     A, |   A3 (MIDI 57)
   342.0s |     E, |   E3 (MIDI 52)
   344.0s |     A, |   A3 (MIDI 57)
   346.0s |     A, |   A3 (MIDI 57)
   348.0s |      d |   D5 (MIDI 74)
   350.0s |      C |   C4 (MIDI 60)
   352.0s |     A, |   A3 (MIDI 57)
   354.0s |     A, |   A3 (MIDI 57)
   356.0s |     D, |   D3 (MIDI 50)
   358.0s |     A, |   A3 (MIDI 57)
   360.0s |     A, |   A3 (MIDI 57)
   362.0s |     A, |   A3 (MIDI 57)
   364.0s |      d |   D5 (MIDI 74)
   366.0s |      B |   B4 (MIDI 71)
   368.0s |      B |   B4 (MIDI 71)
   370.0s |      B |   B4 (MIDI 71)
   372.0s |     D, |   D3 (MIDI 50)
   374.0s |     A, |   A3 (MIDI 57)
   376.0s |     A, |   A3 (MIDI 57)
   378.0s |     A, |   A3 (MIDI 57)
   380.0s |      d |   D5 (MIDI 74)

=== ABC NOTATION ===
X:1
T:Yananlar Var
C:Grup Dergah
M:4/4
L:1/4
Q:1/4=100
K:D
% Melody extracted via spectral analysis
% Each note = dominant pitch in 2-second window

| D, D, A, c | D, A A e | C D, A A | A D, D, D, |
| d d D, A | e f# f# g | A, f D, D, | D, e f# f# |
| D, D, b f | D, D, c A | D, D, D, D, | c b b a |
| a g E, e | D, c b b | A A f# D, | D, D E G |
| d D, E D, | e A c B | E, e A d | E, E, B D |
| B B A D, | F# D, D, D, | c b b D, | C#, g d E, |
| D, F# G G | D, E G A | A d B B | F, D, G d |
| F#, d C B | B F#, G G | D, A, D, c | c a D, g |
| D, e d D, | D, G D, A, | F#, E, A A, | c E, E, D, |
| G G D, D, | D, B B G, | D, G A, E, | D, c B A |
| D, A A, E, | D, D A, G | A, F, A, E, | A, A, d C |
| A, A, D, A, | A, A, d B | B B D, A, | A, A, d |

=== SIMPLE NOTE SEQUENCE (for Ney/Saz) ===
Key: D

  D  A  C  D  A  E  C  D  A  D  A  E  F#  G  A  F  D  E  F#  D  B  F  D  C  A  D  C  B  A  G  E  D  C  B  A  F#  D  E  G  D  E  D  E  A  C  B  E  A  D  E  B  D  B  A  D  F#  D  C  B  D  C#  G  D  E  D  F#  G  D  E  G  A  D  B  F  D  G  D  F#  D  C  B  F#  G  D  A  D  C  A  D  G  D  E  D  G  D  A  F#  E  A  C  E  D  G  D  B  G  D  G  A  E  D  C  B  A  D  A  E  D  A  G  A  F  A  E  A  D  C  A  D  A  D  B  D  A  D


Jetzt rendere ich die ABC-Notation als richtiges Notenbild:

Explanation:
Die Analyse ist jetzt komplett. Ich habe:

1. **Tonart**: D (Dur/Moll modal gemischt, typisch Hüseyni-Makam)
2. **Tempo**: ~100 BPM
3. **ABC-Notation**: Vollständige Melodie in 2-Sekunden-Phrasen über die gesamten 383 Sekunden
4. **Einfache Notenfolge**: Für Ney/Saz zum direkten Nachspielen

Jetzt will ich das als schöne Noten rendern. ABC-Notation kann man mit abcm2ps oder abc2ly in LilyPond konvertieren und dann als PNG rendern. Oder ich nutze einen Online-Dienst. Lass mich checken ob abcm2ps installiert ist, oder ich schreib die ABC-Datei und konvertiere sie.

Ich sollte:
1. Die ABC-Datei speichern
2. Versuchen, sie in Noten zu rendern (entweder lokal mit abcm2ps oder online)

Lass mich das machen.

which abcm2ps 2>/dev/null || which abc2midi 2>/dev/null || echo "NO_ABC_TOOLS"


Explanation: Keine ABC-Tools installiert. Ich kann abcm2ps installieren oder einen anderen Weg finden. Lass mich abcm2ps installieren und dann die ABC-Datei rendern.

apt-get install -y abcm2ps 2>&1 | tail -3


Output: /usr/bin/bash: line 3: apt-get: command not found


Explanation: Kein apt-get, also kein Debian/Ubuntu. Lass mich checken was für ein System das ist und ob es einen anderen Paketmanagergibt.

cat /etc/os-release 2>/dev/null | head -5; which pip pip3 2>/dev/null; python3 -c "import abcm2ps" 2>&1; pip install abcm2ps 2>&1 | tail -5


Output:
NAME="ZimaOS"
BOARD="ZimaCube"
VERSION="v1.6.1"
ID=zimaos
VERSION_ID=1.6.1
/DATA/.local/bin/pip
/DATA/.local/bin/pip3
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'abcm2ps'
ERROR: Could not find a version that satisfies the requirement abcm2ps (from versions: none)

[notice] A new release of pip is available: 26.1.1 -> 26.1.2
[notice] To update, run: python3 -m pip install --upgrade pip
ERROR: No matching distribution found for abcm2ps


Explanation:
Kein abcm2ps auf ZimaOS. Ich muss einen anderen Weg finden, die ABC-Notation in ein Notenbild zu rendern. Optionen:

1. **LilyPond** — `lilypond` könnte installiert sein oder installierbar via pip
2. **music21** — Python library, kann ABC parsen und als MusicXML/Lilypond ausgeben
3. **abc2svg** — JavaScript, könnte im Browser laufen
4. **Manuell mit Python + matplotlib** — Notenlinien zeichnen

Lass mich checken ob lilypond oder music21 verfügbar sind.

which lilypond 2>/dev/null; python3 -c "import music21; print(music21.__version__)" 2>&1; pip install music21 2>&1 | tail -5


Output:
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'music21'

Successfully installed chardet-7.4.3 jsonpickle-4.1.2 music21-10.5.0 webcolors-25.10.0

[notice] A new release of pip is available: 26.1.1 -> 26.1.2
[notice] To update, run: python3 -m pip install --upgrade pip


Explanation:
music21 is installed. Now I can use it to render the ABC notation as sheet music. Let me write a script that takes the ABC notation and renders it as a PNG image using music21 + Lilypond or MuseScore.

Actually, music21 can render to Lilypond and then to PNG if lilypond is installed. Let me check if lilypond is available, or if music21 has a built-in renderer.

python3 -c "from music21 import environment; e = environment.Environment(); print('Lilypond:', e['lilypondPath']); print('Musescore:', e['musicxmlPath'])" 2>&1


Output:
Lilypond: /usr/bin/lilypond
Musescore: /usr/bin/mscore3


Explanation: music21 is installed and it thinks lilypond is at /usr/bin/lilypond. Let me check if lilypond is actually there, and if not, I'll use music21 to render the ABC notation to an image via its built-in capabilities (it can render to PNG using its own notation rendering, or I can use the ABC to MusicXML conversion and then render). Let me first check if lilypond is actually available.

ls -la /usr/bin/lilypond 2>&1; which musescore 2>/dev/null || which mscore3 2>/dev/null || echo "NO_MUSESCORE"


Output:
ls: cannot access '/usr/bin/lilypond': No such file or directory
NO_MUSESCORE


Kein Lilypond/Musescore auf dem System. Ich nutze music21 mit matplotlib zum Rendern:

Explanation: music21 is installed but lilypond and musescore aren't available. I can use music21 to generate a MusicXML file and then convert it to an image using an online renderer, or I can use music21's built-in plotting capabilities (matplotlib) to render the notation directly. Let me try music21's plot method which uses matplotlib to render notation.

from music21 import converter, note, stream, meter, key, tempo, clef
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os

# Parse the ABC file we generated
abc_path = "/tmp/yananlar_var.abc"

# Write ABC file
abc_content = """X:1
T:Yananlar Var
C:Grup Dergah
M:4/4
L:1/4
Q:1/4=100
K:D
| D, D, A, c | D, A A e | C D, A A | A D, D, D, |
| d d D, A | e f# f# g | A, f D, D, | D, e f# f# |
| D, D, b f | D, D, c A | D, D, D, D, | c b b a |
| a g E, e | D, c b b | A A f# D, | D, D E G |
| d D, E D, | e A c B | E, e A d | E, E, B D |
| B B A D, | F# D, D, D, | c b b D, | C#, g d E, |
| D, F# G G | D, E G A | A d B B | F, D, G d |
| F#, d C B | B F#, G G | D, A, D, c | c a D, g |
| D, e d D, | D, G D, A, | F#, E, A A, | c E, E, D, |
| G G D, D, | D, B B G, | D, G A, E, | D, c B A |
| D, A A, E, | D, D A, G | A, F, A, E, | A, A, d C |
| A, A, D, A, | A, A, d B | B B D, A, | A, A, d |
"""

with open(abc_path, 'w') as f:
    f.write(abc_content)

# Parse ABC with music21
try:
    score = converter.parse(abc_path, format='abc')
except Exception as e:
    print(f"ABC parse error: {e}")
    # Try building manually
    score = stream.Score()

    # Build manually from our melody data
    melody_notes = [
        ('D', 3), ('D', 3), ('A', 3), ('C', 5),
        ('D', 3), ('A', 4), ('A', 4), ('E', 5),
        ('C', 4), ('D', 3), ('A', 4), ('A', 4),
        ('A', 4), ('D', 3), ('D', 3), ('D', 3),
        ('D', 5), ('D', 5), ('D', 3), ('A', 4),
        ('E', 5), ('F#', 5), ('F#', 5), ('G', 5),
        ('A', 3), ('F', 5), ('D', 3), ('D', 3),
        ('D', 3), ('E', 5), ('F#', 5), ('F#', 5),
        ('D', 3), ('D', 3), ('B', 5), ('F', 5),
        ('D', 3), ('D', 3), ('C', 5), ('A', 4),
        ('D', 3), ('D', 3), ('D', 3), ('D', 3),
        ('C', 5), ('B', 5), ('B', 5), ('A', 5),
        ('A', 5), ('G', 5), ('E', 3), ('E', 5),
        ('D', 3), ('C', 5), ('B', 5), ('B', 5),
        ('A', 4), ('A', 4), ('F#', 5), ('D', 3),
        ('D', 3), ('D', 4), ('E', 4), ('G', 4),
        ('D', 5), ('D', 3), ('E', 4), ('D', 3),
        ('E', 5), ('A', 4), ('C', 5), ('B', 4),
        ('E', 3), ('E', 5), ('A', 4), ('D', 5),
        ('E', 3), ('E', 3), ('B', 4), ('D', 4),
        ('B', 4), ('B', 4), ('A', 4), ('D', 3),
        ('F#', 4), ('D', 3), ('D', 3), ('D', 3),
        ('C', 5), ('B', 5), ('B', 5), ('D', 3),
        ('C#', 3), ('G', 5), ('D', 5), ('E', 3),
        ('D', 3), ('F#', 4), ('G', 4), ('G', 4),
        ('D', 3), ('E', 4), ('G', 4), ('A', 4),
        ('A', 4), ('D', 5), ('B', 4), ('B', 4),
        ('F', 3), ('D', 3), ('G', 4), ('D', 5),
        ('F#', 3), ('D', 5), ('C', 4), ('B', 4),
        ('B', 4), ('F#', 3), ('G', 4), ('G', 4),
        ('D', 3), ('A', 3), ('D', 3), ('C', 5),
        ('C', 5), ('A', 5), ('D', 3), ('G', 5),
        ('D', 3), ('E', 5), ('D', 5), ('D', 3),
        ('D', 3), ('G', 4), ('D', 3), ('A', 3),
        ('F#', 3), ('E', 3), ('A', 4), ('A', 3),
        ('C', 5), ('E', 3), ('E', 3), ('D', 3),
        ('G', 4), ('G', 4), ('D', 3), ('D', 3),
        ('D', 3), ('B', 4), ('B', 4), ('G', 3),
        ('D', 3), ('G', 4), ('A', 3), ('E', 3),
        ('D', 3), ('C', 5), ('B', 4), ('A', 4),
        ('D', 3), ('A', 4), ('A', 3), ('E', 3),
        ('D', 3), ('D', 4), ('A', 3), ('G', 4),
        ('A', 3), ('F', 3), ('A', 3), ('E', 3),
        ('A', 3), ('A', 3), ('D', 5), ('C', 4),
        ('A', 3), ('A', 3), ('D', 3), ('A', 3),
        ('A', 3), ('A', 3), ('D', 5), ('B', 4),
        ('B', 4), ('B', 4), ('D', 3), ('A', 3),
        ('A', 3), ('A', 3), ('D', 5),
    ]

    part = stream.Part()
    part.append(clef.TrebleClef())
    part.append(key.Key('D'))
    part.append(meter.TimeSignature('4/4'))
    part.append(tempo.MetronomeMark(number=100))

    m = stream.Measure()
    count = 0
    for pitch, octave in melody_notes:
        n = note.Note(pitch + str(octave))
        n.quarterLength = 1.0
        m.append(n)
        count += 1
        if count % 4 == 0:
            part.append(m)
            m = stream.Measure()

    if count % 4 != 0:
        part.append(m)

    score.insert(0, part)

# Write MusicXML
xml_path = "/tmp/yananlar_var.musicxml"
score.write('musicxml', fp=xml_path)
print(f"MusicXML written to {xml_path}")

# Also try to render with music21's plot
try:
    # Render first 4 measures as PNG
    measures_to_show = score.parts[0].getElementsByClass('Measure')[:12]
    short_score = stream.Score()
    short_part = stream.Part()
    short_part.append(clef.TrebleClef())
    short_part.append(key.Key('D'))
    short_part.append(meter.TimeSignature('4/4'))
    for m in measures_to_show:
        short_part.append(m)
    short_score.insert(0, short_part)

    # Write Lilypond and try to render
    ly_path = "/tmp/yananlar_var.ly"
    short_score.write('lilypond', fp=ly_path)
    print(f"LilyPond written to {ly_path}")

    # Also generate PNG via music21's lilypond conversion if possible
    png_path = "/tmp/yananlar_var.png"
    # music21 can try to call lilypond
    try:
        short_score.write('lily.png', fp=png_path)
        print(f"PNG written to {png_path}")
    except Exception as e2:
        print(f"PNG render failed: {e2}")

except Exception as e:
    print(f"Render error: {e}")

print("Done!")


Explanation: Let me run the rendering script and see what happens.


Output:
mkdir -p failed for path /DATA/.config/matplotlib: [Errno 13] Permission denied: '/DATA/.config'
Matplotlib created a temporary cache directory at /tmp/matplotlib-pljzxph7 because there was an issue with the default path (/DATA/.config/matplotlib); it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.
Fontconfig warning: "/usr/share/fontconfig/conf.avail/44-wqy-zenhei.conf", line 11: Having multiple values in <test> isn't supported and may not work as expected
MusicXML written to /tmp/yananlar_var.musicxml
Render error: Cannot find a copy of Lilypond installed on your system. Please be sure it is installed. And that your environment.UserSettings()['lilypondPath'] is set to find it.
Done!


Lilypond fehlt auf dem System. Ich versuche einen Online-Renderer:

Explanation:
Lilypond isn't installed, so I can't render sheet music as a PNG. But I have the MusicXML file and the ABC notation. Let me try an alternative approach - maybe I can use a web-based ABC renderer, or try to install lilypond via pip, or use a different approach.

Let me check if there's a Python-only ABC renderer, or try to install lilypond via some other method. Actually, let me try `pip install lilypond` or check if there's a way to get it on this system.

Actually, the best approach now is to just give the user the ABC notation and MusicXML file. The user can paste the ABC into any online ABC renderer (like abcnotation.com, folkinfo.org, or mandolintab.net) to get proper sheet music. Or they can open the MusicXML in MuseScore.

Let me also try to generate a simple visual representation using matplotlib directly - drawing a staff and notes manually. That would be a lot of work though.

Actually, let me try one more thing - maybe I can use `abcm2ps` via a Python wrapper or compile it. Or check if there's a web service I can POST the ABC to.

Let me try the simplest approach: give the user the ABC notation and tell them how to render it. Also provide the MusicXML file. And I can try to use an online ABC renderer via web_search.

Actually, let me try one more thing - `pip install abctools` or similar.

pip install abctools 2>&1 | tail -3; python3 -c "import abctools" 2>&1


Output:
[notice] A new release of pip is available: 26.1.1 -> 26.1.2
[notice] To update, run: python3 -m pip install --upgrade pip
ERROR: No matching distribution found for abctools
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'abctools'


Explanation:
Let me try a different approach. I can use the ABC notation and render it via an online service, or I can try to install lilypond manually. Let me check if there's a way to get lilypond on this system.

Actually, let me try a simpler approach: use the `abc2svg` JavaScript library via a headless browser, or just use an online ABC renderer API.

Or better yet - let me try to install lilypond via pip or conda, or download a static binary.

Actually, the simplest approach: I'll create an HTML file with abc2svg that renders the notation, then use a headless browser to screenshot it. But we don't have a browser tool available directly.

Let me try another approach: use the `verovio` toolkit which can render MEI/MusicXML to SVG.

Or even simpler: I can use an online ABC renderer service. Let me try posting the ABC to abcnotation.com's renderer or use a public API.

Actually, let me just try to install lilypond. It might be available as a static binary.

# Try downloading lilypond static binary
curl -L "https://gitlab.com/lilypond/lilypond/-/releases/v2.24.4/downloads/lilypond-2.24.4-linux-x86_64.tar.gz" -o /tmp/lilypond.tar.gz 2>&1 | tail -5


Output:
% Total    % Received % Xferd  Average Speed  Time    Time    Time   Current
                                 Dload  Upload  Total   Spent   Left   Speed

  0      0   0      0   0      0      0      0                              0
100    189 100    189   0      0    732      0                              0
100    189 100    189   0      0    731      0                              0

  0      0   0      0   0      0      0      0                              0
 32 38.31M  32 12.59M   0      0 10.01M      0   00:03   00:01   00:02 12.59M
100 38.31M 100 38.31M   0      0 18.13M      0   00:02   00:02         12.59M
100 38.31M 100 38.31M   0      0 18.13M      0   00:02   00:02         12.59M
100 38.31M 100 38.31M   0      0 18.13M      0   00:02   00:02         12.59M


Explanation: Lilypond binary downloaded. Let me extract it and use it to render the sheetmusic.

cd /tmp && tar xzf lilypond.tar.gz && ls -d lilypond-* 2>&1


Explanation: Lilypond is now available at /tmp/lilypond-2.24.4/bin/lilypond. Let me use it to render the ABC notation to a proper sheet music PNG.

# Convert ABC to LilyPond using music21, then render with lilypond binary
python3 << 'PYEOF'
import os
os.environ['MPLCONFIGDIR'] = '/tmp/mplconfig'

from music21 import converter, environment

# Set lilypond path
env = environment.Environment()
env['lilypondPath'] = '/tmp/lilypond-2.24.4/bin/lilypond'

# Parse ABC
score = converter.parse('/tmp/yananlar_var.abc', format='abc')

# Write lilypond
ly_path = '/tmp/yananlar_var.ly'
score.write('lilypond', fp=ly_path)
print(f"LilyPond file written: {ly_path}")

# Now render with lilypond binary
import subprocess
result = subprocess.run(
    ['/tmp/lilypond-2.24.4/bin/lilypond', '--png', '-o', '/tmp/yananlar_var', ly_path],
    capture_output=True, text=True, timeout=60, cwd='/tmp'
)
print("STDOUT:", result.stdout[-500:] if result.stdout else "")
print("STDERR:", result.stderr[-500:] if result.stderr else "")
print("Return code:", result.returncode)

# Check for output
import glob
pngs = glob.glob('/tmp/yananlar_var*.png')
print(f"PNG files: {pngs}")
PYEOF


Output:
LilyPond file written: /tmp/yananlar_var.ly
STDOUT:
STDERR: Processing `/tmp/yananlar_var.ly'
Parsing...
Interpreting music...[8][16][24][32][40][48]
Preprocessing graphical objects...
Calculating line breaks...
Drawing systems...
Converting to PNG...
Success: compilation successfully completed

Return code: 0
PNG files: ['/tmp/yananlar_var-page7.png', '/tmp/yananlar_var-page6.png', '/tmp/yananlar_var-page5.png', '/tmp/yananlar_var-page4.png', '/tmp/yananlar_var-page3.png', '/tmp/yananlar_var-page2.png', '/tmp/yananlar_var-page1.png']


Noten sind fertig! Hier die erste Seite als Vorschau:

MEDIA:/tmp/yananlar_var-page1.png

---

## 🎼 „Yananlar Var" — Noten (automatisch extrahiert)

**Tonart:** D (Hüseyni-Makam)
**Tempo:** ~100 BPM (4/4)
**7 Seiten** Noten — alle PNGs unter `/tmp/yananlar_var-page*.png`

**Wichtiger Hinweis:** Das ist eine **maschinelle Spektral-Analyse**, keine perfekte Transkription. Die Melodie wurde aus dem Audio extrahiert (dominante Frequenz pro 2-Sekunden-Fenster). Bei İrfan-Türküler mit melismatischem Gesang und Drohne-Begleitung ist das schwierig — die Notation gibt die **harmonische Struktur und Melodierichtung** wieder, aber Mikro-Verzierungen und exakte Rhythmen sind vereinfacht.

**Dateien die du bekommst:**
- `/tmp/yananlar_var-page1.png` bis `page7.png` — Notenbilder
- `/tmp/yananlar_var.abc` — ABC-Notation (editierbar)
- `/tmp/yananlar_var.musicxml` — MusicXML (für MuseScore etc.)

Soll ich dir alle 7 Seiten schicken, oder reicht dir das als Grundlage zum Weiterspielen?

Explanation:
LilyPond hat 7 Seiten PNGs generiert. Ich sollte die erste Seite als Vorschau zeigen und dem Boss erklären, was er bekommen hat. Die ABC-Notation ist eine automatische Extraktion — ich sollte ehrlich sein, dass es eine maschinelle Analyse ist und keine perfekte Transkription.

Lass mich die erste Seite anzeigen und dann eine Zusammenfassung geben.


https://jobs.derstandard.at/detail/520db18b-7a95-3484-b1c9-75f73e347531

Schreibe mir ein Motivatilnsschreiben für diese Stelle, begründe meine ganzen Tätigkeiten mit AI (Agenten etc) und auch warum ich trotz noch keinen abgeschlossenen Studium (wovor ich aber kurz stehe, geplant ist Ende des Jahres LLB) perfekt geeignet bin. Lies dafür auch alle meinen Bewerbungsunterlagen auf Notion


Explanation:
Der Boss möchte ein Motivationsschreiben für eine Stelle auf derstandard.at. Er will, dass ich:

1. Die Stellenausschreibung lese
2. Seine Bewerbungsunterlagen auf Notion lese
3. Ein Motivationsschreiben verfasse, das:
   - Seine AI-Tätigkeiten (Agenten etc.) als Qualifikation darstellt
   - Begründet, warum er trotz noch nicht abgeschlossenem Studium (LLB, geplant Ende des Jahres) perfekt geeignet ist

Ich brauche:
- Den Job-Link zu lesen
- Zugriff auf Notion für seine Bewerbungsunterlagen
- Skills: notion, google-workspace (vielleicht), und research-literature für akademisches Profil

Lass mich starten: Link lesen, Notion checken, dann schreiben.