Use different binaries for feature extraction

__init__.py

@@ -1,4 +1,5 @@
 import os
+import re
 import json
 import shutil
 import subprocess
@@ -8,10 +9,10 @@ import struct
 import threading
 import concurrent.futures
 import tempfile
-
+import math
+import yaml
 from functools import partial
 from typing import List, Tuple, Dict, Optional
-
 from picard import config, log
 from picard.ui.itemviews import (
     BaseAction,
@@ -22,10 +23,7 @@ from picard.track import Track
 from picard.album import Album
 from picard.ui.options import OptionsPage, register_options_page
 from picard.util import thread
-from picard.coverart.image import (
+from picard.coverart.image import CoverArtImage
-    CoverArtImage,
-    CoverArtImageError,
-)
 from PyQt5 import QtWidgets, QtCore
 
 _analysis_semaphore = None
@@ -56,20 +54,23 @@ class AcousticBrainzNG:
             binary_path += '.exe'
         return binary_path
 
-    def _get_binary_paths(self) -> Tuple[str, str]:
+    def _get_binary_paths(self) -> Tuple[str, str, str]:
         binaries_path = config.setting["acousticbrainz_ng_binaries_path"]
         if not binaries_path:
             raise ValueError("Binaries path not configured")
             
         musicnn_binary_path = self._get_binary_path("streaming_musicnn_predict", binaries_path)
-        gaia_binary_path = self._get_binary_path("streaming_extractor_music", binaries_path)
+        rhythm_binary_path = self._get_binary_path("streaming_rhythmextractor_multifeature", binaries_path)
+        key_binary_path = self._get_binary_path("streaming_key", binaries_path)
 
         if not os.path.exists(musicnn_binary_path):
             raise FileNotFoundError(f"Binary {musicnn_binary_path} not found")
-        if not os.path.exists(gaia_binary_path):
+        if not os.path.exists(rhythm_binary_path):
-            raise FileNotFoundError(f"Binary {gaia_binary_path} not found")
+            raise FileNotFoundError(f"Binary {rhythm_binary_path} not found")
+        if not os.path.exists(key_binary_path):
+            raise FileNotFoundError(f"Binary {key_binary_path} not found")
 
-        return musicnn_binary_path, gaia_binary_path
+        return musicnn_binary_path, rhythm_binary_path, key_binary_path
 
     def _run_musicnn_models(self, models: List[Tuple[str, str]], musicnn_binary_path: str, file: str, output_path: str) -> bool:
         models_path = config.setting["acousticbrainz_ng_models_path"]
@@ -133,7 +134,7 @@ class AcousticBrainzNG:
             return False
         
         try:
-            musicnn_binary_path, gaia_binary_path = self._get_binary_paths()
+            musicnn_binary_path, rhythm_binary_path, key_binary_path = self._get_binary_paths()
         except (ValueError, FileNotFoundError) as e:
             log.error(str(e))
             return False
@@ -147,80 +148,86 @@ class AcousticBrainzNG:
             log.error(f"Error generating cache folder: {e}")
             return False
 
-        gaia_success = True
+        rhythm_success = True
-        def run_gaia():
+        def run_rhythm():
-            nonlocal gaia_success
+            nonlocal rhythm_success
-            if os.path.exists(os.path.join(output_path, "gaia.json")):
+            if os.path.exists(os.path.join(output_path, "rhythm.yaml")):
                 return
 
-            jq_path = config.setting["acousticbrainz_ng_jq_path"]
+            rhythm_proc = subprocess.run(
-            if not jq_path or not os.path.exists(jq_path):
+                [rhythm_binary_path, file],
-                log.error("jq binary path not configured or invalid")
-                gaia_success = False
-                return
-
-            gaia_proc = subprocess.run(
-                [gaia_binary_path, file, "-"],
                 capture_output=True,
                 text=True,
                 env=ENV,
                 creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
             )
             
-            if gaia_proc.returncode != 0:
+            if rhythm_proc.returncode != 0:
-                gaia_success = False
+                rhythm_success = False
-                log.error(f"Gaia binary {gaia_binary_path} failed on file {file} with exit code {gaia_proc.returncode}")
+                log.error(f"Rhythm binary {rhythm_binary_path} failed on file {file} with exit code {rhythm_proc.returncode}")
-                if gaia_proc.stdout:
+                if rhythm_proc.stdout:
-                    log.error(f"Gaia stdout: {gaia_proc.stdout}")
+                    log.error(f"Rhythm stdout: {rhythm_proc.stdout}")
-                if gaia_proc.stderr:
+                if rhythm_proc.stderr:
-                    log.error(f"Gaia stderr: {gaia_proc.stderr}")
+                    log.error(f"Rhythm stderr: {rhythm_proc.stderr}")
-                return
-
-            jq_filter = (
-                "{ rhythm: { bpm: .rhythm.bpm }, "
-                "tonal: { "
-                "chords_changes_rate: .tonal.chords_changes_rate, "
-                "chords_key: .tonal.chords_key, "
-                "chords_scale: .tonal.chords_scale, "
-                "key_temperley: { key: .tonal.key_temperley.key, scale: .tonal.key_temperley.scale, strength: .tonal.key_temperley.strength }, "
-                "key_krumhansl: { key: .tonal.key_krumhansl.key, scale: .tonal.key_krumhansl.scale, strength: .tonal.key_krumhansl.strength }, "
-                "key_edma: { key: .tonal.key_edma.key, scale: .tonal.key_edma.scale, strength: .tonal.key_edma.strength } "
-                "} }"
-            )
-
-            jq_proc = subprocess.run(
-                [jq_path, jq_filter],
-                input=gaia_proc.stdout,
-                capture_output=True,
-                text=True,
-                env=ENV,
-                creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
-            )
-
-            if jq_proc.returncode != 0:
-                gaia_success = False
-                log.error(f"jq failed to post-process Gaia JSON with exit code {jq_proc.returncode}")
-                if jq_proc.stdout:
-                    log.error(f"jq stdout: {jq_proc.stdout}")
-                if jq_proc.stderr:
-                    log.error(f"jq stderr: {jq_proc.stderr}")
                 return
 
             try:
-                os.makedirs(output_path, exist_ok=True)
+                stdout = rhythm_proc.stdout or ""
-                with open(os.path.join(output_path, "gaia.json"), "w", encoding="utf-8") as f:
+                lines = stdout.splitlines(keepends=True)
-                    f.write(jq_proc.stdout)
+                if not lines:
-            except Exception as e:
+                    raise ValueError("Rhythm binary produced no stdout")
-                gaia_success = False
-                log.error(f"Failed to write processed Gaia JSON: {e}")
 
-        gaia_thread = threading.Thread(target=run_gaia)
+                yaml_lines = lines[-5:] if len(lines) >= 5 else lines
-        gaia_thread.start()
+                yaml_str = "".join(yaml_lines)
+                if not yaml_str.strip():
+                    raise ValueError("Empty YAML section extracted from rhythm binary output")
+
+                out_file = os.path.join(output_path, "rhythm.yaml")
+                with open(out_file, "w", encoding="utf-8") as f:
+                    f.write(yaml_str)
+            except Exception as e:
+                rhythm_success = False
+                log.error(f"Failed to extract/save rhythm.yaml from rhythm binary stdout: {e}")
+                if rhythm_proc.stdout:
+                    log.error(f"Rhythm stdout: {rhythm_proc.stdout}")
+                if rhythm_proc.stderr:
+                    log.error(f"Rhythm stderr: {rhythm_proc.stderr}")
+                return
+        
+        key_success = True
+        def run_key():
+            nonlocal key_success
+            if os.path.exists(os.path.join(output_path, "key.yaml")):
+                return
+
+            key_proc = subprocess.run(
+                [key_binary_path, file, os.path.join(output_path, "key.yaml")],
+                capture_output=True,
+                text=True,
+                env=ENV,
+                creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
+            )
+
+            if key_proc.returncode != 0:
+                key_success = False
+                log.error(f"Key binary {key_binary_path} failed on file {file} with exit code {key_proc.returncode}")
+                if key_proc.stdout:
+                    log.error(f"Key stdout: {key_proc.stdout}")
+                if key_proc.stderr:
+                    log.error(f"Key stderr: {key_proc.stderr}")
+                return
+
+        rhythm_thread = threading.Thread(target=run_rhythm)
+        rhythm_thread.start()
+
+        key_thread = threading.Thread(target=run_key)
+        key_thread.start()
 
         musicnn_success = self._run_musicnn_models(REQUIRED_MODELS, musicnn_binary_path, file, output_path)
-        gaia_thread.join()
+        rhythm_thread.join()
+        key_thread.join()
 
-        return gaia_success and musicnn_success
+        return rhythm_success and key_success and musicnn_success
 
     def analyze_optional(self, metadata: Dict, file: str) -> bool:        
         if not self._check_binaries():
@@ -232,7 +239,7 @@ class AcousticBrainzNG:
             return False
         
         try:
-            musicnn_binary_path, _ = self._get_binary_paths()
+            musicnn_binary_path = self._get_binary_paths()[0]
         except (ValueError, FileNotFoundError) as e:
             log.error(str(e))
             return False
@@ -329,50 +336,48 @@ class AcousticBrainzNG:
         metadata['mood'] = moods
         metadata['tags'] = tags
         
-        gaia_data = {}
+        rhythm_data = {}
-        gaia_json_path = os.path.join(output_path, "gaia.json")
+        rhythm_yaml_path = os.path.join(output_path, "rhythm.yaml")
 
-        if os.path.exists(gaia_json_path):
+        key_data = {}
-            try:
+        key_yaml_path = os.path.join(output_path, "key.yaml")
-                with open(gaia_json_path, 'r', encoding='utf-8') as f:
+
-                    gaia_data = json.load(f)
+        if os.path.exists(rhythm_yaml_path):
-            except (FileNotFoundError, json.JSONDecodeError) as e:
+            with open(rhythm_yaml_path, 'r', encoding='utf-8') as f:
-                log.error(f"Error reading Gaia JSON file: {e}")
+                loaded = yaml.safe_load(f)
+
+            if not isinstance(loaded, dict):
+                log.error("Invalid rhythm YAML format: expected a mapping at the top level")
                 return False
+
+            rhythm_data = loaded
         else:
-            log.error(f"Gaia JSON file not found: {gaia_json_path}")
+            log.error(f"Rhythm YAML file not found: {rhythm_yaml_path}")
+            return False
+
+        if os.path.exists(key_yaml_path):
+            with open(key_yaml_path, 'r', encoding='utf-8') as f:
+                loaded = yaml.safe_load(f)
+            if not isinstance(loaded, dict):
+                log.error("Invalid key YAML format: expected a mapping at the top level")
+                return False
+
+            key_data = loaded
+        else:
+            log.error(f"Key YAML file not found: {key_yaml_path}")
             return False
 
         try:
-            metadata["bpm"] = int(round(gaia_data["rhythm"]["bpm"]))
+            metadata["bpm"] = int(round(rhythm_data["bpm"]))
+            metadata["key"] = "o" if key_data["tonal"]["key_scale"] == "off" else f"{key_data['tonal']['key']}{'m' if key_data['tonal']['key_scale'] == 'minor' else ''}"
 
             if config.setting["acousticbrainz_ng_save_raw"]:
-                metadata["ab:lo:tonal:chords_changes_rate"] = gaia_data["tonal"]["chords_changes_rate"]
+                metadata["ab:lo:tonal:key_scale"] = key_data["tonal"]["key_scale"]
-                metadata["ab:lo:tonal:chords_key"] = gaia_data["tonal"]["chords_key"]
+                metadata["ab:lo:tonal:key_key"] = key_data["tonal"]["key"]
-                metadata["ab:lo:tonal:chords_scale"] = gaia_data["tonal"]["chords_scale"]
-            
-            highestStrength = -1
-            selectedAlgorithm = None
-            
-            for algorithm in GAIA_KEY_ALGORITHMS:
-                key_data = gaia_data["tonal"][f"key_{algorithm}"]
-
-                if key_data["strength"] > highestStrength:
-                    highestStrength = key_data["strength"]
-                    selectedAlgorithm = algorithm
-            
-            if selectedAlgorithm:
-                selected_key_data = gaia_data["tonal"][f"key_{selectedAlgorithm}"]
-
-                metadata["key"] = "o" if selected_key_data["scale"] == "off" else f"{selected_key_data['key']}{'m' if selected_key_data['scale'] == 'minor' else ''}"
-
-                if config.setting["acousticbrainz_ng_save_raw"]:
-                    metadata["ab:lo:tonal:key_scale"] = selected_key_data["scale"]
-                    metadata["ab:lo:tonal:key_key"] = selected_key_data["key"]
             
             return True
         except Exception as e:
-            log.error(f"Error processing gaia data: {e}")
+            log.error(f"Error processing feature data: {e}")
             return False
     
     def parse_optional(self, metadata: Dict, file: str) -> bool:
@@ -671,7 +676,7 @@ class AcousticBrainzNG:
             if not ffmpeg_path:
                 raise ValueError("FFmpeg path not configured")
             
-            replaygain_lufs_result = subprocess.run(
+            replaygain_proc = subprocess.run(
                 [ffmpeg_path, "-hide_banner", "-i", file_path, "-af", f"loudnorm=I={config.setting['acousticbrainz_ng_replaygain_reference_loudness']}:print_format=json", "-f", "null", "-"],
                 capture_output=True,
                 text=True,
@@ -679,40 +684,66 @@ class AcousticBrainzNG:
                 creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
             )
             
-            if replaygain_lufs_result.returncode != 0:
+            if replaygain_proc.returncode != 0:
-                log.error(f"FFmpeg failed for ReplayGain LUFS calculation on file {file_path} with exit code {replaygain_lufs_result.returncode}")
+                log.error(f"FFmpeg failed for ReplayGain LUFS calculation on file {file_path} with exit code {replaygain_proc.returncode}")
-                if replaygain_lufs_result.stdout:
+                if replaygain_proc.stdout:
-                    log.error(f"FFmpeg stdout: {replaygain_lufs_result.stdout}")
+                    log.error(f"FFmpeg stdout: {replaygain_proc.stdout}")
-                if replaygain_lufs_result.stderr:
+                if replaygain_proc.stderr:
-                    log.error(f"FFmpeg stderr: {replaygain_lufs_result.stderr}")
+                    log.error(f"FFmpeg stderr: {replaygain_proc.stderr}")
                 return {}
             
+            replaygain_log = replaygain_proc.stderr or replaygain_proc.stdout
+            
+            replaygain_match = re.search(r'\{.*?\}', replaygain_log, re.S)
+            replaygain_matches = re.findall(r'\{.*?\}', replaygain_log, re.S) if not replaygain_match else None
+            replaygain_json_text = replaygain_match.group(0) if replaygain_match else (replaygain_matches[0] if replaygain_matches else None)
+            
             replaygain_gain = None
             replaygain_peak = None
             replaygain_range = None
             
+            replaygain_lufs_result: dict | None = None
+            
+            if replaygain_json_text:
                 try:
-                json_start = replaygain_lufs_result.stderr.find('{')
+                    replaygain_lufs_result = json.loads(replaygain_json_text)
-                if json_start != -1:
+                except json.JSONDecodeError:
-                    json_str = replaygain_lufs_result.stderr[json_start:]
+                    if replaygain_matches:
-                    json_end = json_str.find('}') + 1
+                        try:
-                    if json_end > 0:
+                            replaygain_lufs_result = json.loads(replaygain_matches[-1])
-                        loudnorm_data = json.loads(json_str[:json_end])
+                        except Exception:
-                        input_i = loudnorm_data.get('input_i')
+                            replaygain_lufs_result = None
-                        input_tp = loudnorm_data.get('input_tp')
-                        input_lra = loudnorm_data.get('input_lra')
 
-                        if input_i and input_i != "-inf":
+            input_i = replaygain_lufs_result.get('input_i') if replaygain_lufs_result else None
-                            replaygain_gain = f"{(config.setting['acousticbrainz_ng_replaygain_reference_loudness'] or -18) - float(input_i):.2f}"
+            input_tp = replaygain_lufs_result.get('input_tp') if replaygain_lufs_result else None
+            input_lra = replaygain_lufs_result.get('input_lra') if replaygain_lufs_result else None
             
-                        if input_tp and input_tp != "-inf":
+            try:
-                            replaygain_peak = f"{10 ** (float(input_tp) / 20):.6f}"
+                if input_i:
+                    input_i_val = float(input_i)
+            except (TypeError, ValueError):
+                input_i_val = None
 
-                        if input_lra and input_lra != "-inf":
+            try:
-                            replaygain_range = f"{float(input_lra):.2f}"
+                if input_tp:
+                    input_tp_val = float(input_tp)
+            except (TypeError, ValueError):
+                input_tp_val = None
             
-            except (json.JSONDecodeError, ValueError, TypeError):
+            try:
-                pass
+                if input_lra:
+                    input_lra_val = float(input_lra)
+            except (TypeError, ValueError):
+                input_lra_val = None
+
+            if input_i_val is not None and math.isfinite(input_i_val):
+                replaygain_gain = f"{(config.setting['acousticbrainz_ng_replaygain_reference_loudness'] or -18) - input_i_val:.2f}"
+
+            if input_tp_val is not None and math.isfinite(input_tp_val):
+                replaygain_peak = f"{10 ** (input_tp_val / 20):.6f}"
+
+            if input_lra_val is not None and math.isfinite(input_lra_val):
+                replaygain_range = f"{input_lra_val:.2f}"
             
             result: Dict = {
                 "replaygain_track_gain": replaygain_gain,
@@ -721,7 +752,7 @@ class AcousticBrainzNG:
                 "replaygain_reference_loudness": f"{(config.setting['acousticbrainz_ng_replaygain_reference_loudness'] or -18):.2f}"
             }
             
-            r128_result = subprocess.run(
+            r128_proc = subprocess.run(
                 [ffmpeg_path, "-hide_banner", "-i", file_path, "-af", "loudnorm=I=-23:print_format=json", "-f", "null", "-"],
                 capture_output=True,
                 text=True,
@@ -729,27 +760,44 @@ class AcousticBrainzNG:
                 creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
             )
             
-            if r128_result.returncode != 0:
+            if r128_proc.returncode != 0:
-                log.error(f"FFmpeg failed for R128 calculation on file {file_path} with exit code {r128_result.returncode}")
+                log.error(f"FFmpeg failed for R128 calculation on file {file_path} with exit code {r128_proc.returncode}")
-                if r128_result.stdout:
+                if r128_proc.stdout:
-                    log.error(f"FFmpeg stdout: {r128_result.stdout}")
+                    log.error(f"FFmpeg stdout: {r128_proc.stdout}")
-                if r128_result.stderr:
+                if r128_proc.stderr:
-                    log.error(f"FFmpeg stderr: {r128_result.stderr}")
+                    log.error(f"FFmpeg stderr: {r128_proc.stderr}")
                 return result
 
+            r128_log = r128_proc.stderr or r128_proc.stdout
+
+            r128_match = re.search(r'\{.*?\}', r128_log, re.S)
+            r128_matches = re.findall(r'\{.*?\}', r128_log, re.S) if not r128_match else None
+            r128_json_text = r128_match.group(0) if r128_match else (r128_matches[0] if r128_matches else None)
+
             r128_track_gain = None
 
-            try:
+            r128_data: dict | None = None
-                json_start = r128_result.stderr.find('{')
-                if json_start != -1:
-                    json_str = r128_result.stderr[json_start:]
-                    json_end = json_str.find('}') + 1
-                    if json_end > 0:
-                        r128_data = json.loads(json_str[:json_end])
-                        r128_input_i = r128_data.get('input_i')
             
-                        if r128_input_i and r128_input_i != "-inf":
+            if r128_json_text:
-                            r128_gain_db = -23 - float(r128_input_i)
+                try:
+                    r128_data = json.loads(r128_json_text)
+                except json.JSONDecodeError:
+                    if r128_matches:
+                        try:
+                            r128_data = json.loads(r128_matches[-1])
+                        except Exception:
+                            r128_data = None
+
+            r128_input_i = r128_data.get('input_i') if r128_data else None
+
+            try:
+                if r128_input_i:
+                    r128_input_i_val = int(r128_input_i)
+            except (TypeError, ValueError):
+                r128_input_i_val = None
+
+            if r128_input_i_val is not None and math.isfinite(r128_input_i_val):
+                r128_gain_db = -23 - r128_input_i_val
                 r128_track_gain = int(round(r128_gain_db * 256))
                 
                 if r128_track_gain < -32768:
@@ -757,9 +805,6 @@ class AcousticBrainzNG:
                 elif r128_track_gain > 32767:
                     r128_track_gain = 32767
             
-            except (json.JSONDecodeError, ValueError, TypeError):
-                pass
-            
             result["r128_track_gain"] = r128_track_gain
             
             return result
@@ -787,7 +832,7 @@ class AcousticBrainzNG:
                 concat_file_path = concat_file.name
             
             try:
-                album_replaygain_result = subprocess.run(
+                album_replaygain_proc = subprocess.run(
                     [ffmpeg_path, "-hide_banner", "-f", "concat", "-safe", "0", "-i", concat_file_path, 
                      "-vn", "-af", f"loudnorm=I={config.setting['acousticbrainz_ng_replaygain_reference_loudness']}:print_format=json", "-f", "null", "-"],
                     capture_output=True,
@@ -796,41 +841,67 @@ class AcousticBrainzNG:
                     creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
                 )
                 
-                if album_replaygain_result.returncode != 0:
+                if album_replaygain_proc.returncode != 0:
-                    log.error(f"FFmpeg failed for album ReplayGain calculation on {len(album_track_files)} files with exit code {album_replaygain_result.returncode}")
+                    log.error(f"FFmpeg failed for album ReplayGain calculation on {len(album_track_files)} files with exit code {album_replaygain_proc.returncode}")
                     log.error(f"Album files: {', '.join(album_track_files)}")
-                    if album_replaygain_result.stdout:
+                    if album_replaygain_proc.stdout:
-                        log.error(f"FFmpeg stdout: {album_replaygain_result.stdout}")
+                        log.error(f"FFmpeg stdout: {album_replaygain_proc.stdout}")
-                    if album_replaygain_result.stderr:
+                    if album_replaygain_proc.stderr:
-                        log.error(f"FFmpeg stderr: {album_replaygain_result.stderr}")
+                        log.error(f"FFmpeg stderr: {album_replaygain_proc.stderr}")
                     return {}
 
+                album_replaygain_log = album_replaygain_proc.stderr or album_replaygain_proc.stdout
+
+                album_replaygain_match = re.search(r'\{.*?\}', album_replaygain_log, re.S)
+                album_replaygain_matches = re.findall(r'\{.*?\}', album_replaygain_log, re.S) if not album_replaygain_match else None
+                album_replaygain_json_text = album_replaygain_match.group(0) if album_replaygain_match else (album_replaygain_matches[0] if album_replaygain_matches else None)
+
                 album_gain = None
                 album_peak = None
                 album_range = None
                 
+                loudnorm_data: dict | None = None
+                
+                if album_replaygain_json_text:
                     try:
-                    json_start = album_replaygain_result.stderr.find('{')
+                        loudnorm_data = json.loads(album_replaygain_json_text)
-                    if json_start != -1:
+                    except json.JSONDecodeError:
-                        json_str = album_replaygain_result.stderr[json_start:]
+                        if album_replaygain_matches:
-                        json_end = json_str.find('}') + 1
+                            try:
-                        if json_end > 0:
+                                loudnorm_data = json.loads(album_replaygain_matches[-1])
-                            loudnorm_data = json.loads(json_str[:json_end])
+                            except Exception:
-                            input_i = loudnorm_data.get('input_i')
+                                loudnorm_data = None
-                            input_tp = loudnorm_data.get('input_tp')
-                            input_lra = loudnorm_data.get('input_lra')
                 
-                            if input_i and input_i != "-inf":
+                input_i = loudnorm_data.get('input_i') if loudnorm_data else None
-                                album_gain = f"{(config.setting['acousticbrainz_ng_replaygain_reference_loudness'] or -18) - float(input_i):.2f}"
+                input_tp = loudnorm_data.get('input_tp') if loudnorm_data else None
+                input_lra = loudnorm_data.get('input_lra') if loudnorm_data else None
                 
-                            if input_tp and input_tp != "-inf":
+                try:
-                                album_peak = f"{10 ** (float(input_tp) / 20):.6f}"
+                    if input_i:
+                        input_i_val = float(input_i)
+                except (TypeError, ValueError):
+                    input_i_val = None
                 
-                            if input_lra and input_lra != "-inf":
+                try:
-                                album_range = f"{float(input_lra):.2f}"
+                    if input_tp:
+                        input_tp_val = float(input_tp)
+                except (TypeError, ValueError):
+                    input_tp_val = None
                 
-                except (json.JSONDecodeError, ValueError, TypeError):
+                try:
-                    pass
+                    if input_lra:
+                        input_lra_val = float(input_lra)
+                except (TypeError, ValueError):
+                    input_lra_val = None
+
+                if input_i_val is not None and math.isfinite(input_i_val):
+                    album_gain = f"{(config.setting['acousticbrainz_ng_replaygain_reference_loudness'] or -18) - input_i_val:.2f}"
+
+                if input_tp_val is not None and math.isfinite(input_tp_val):
+                    album_peak = f"{10 ** (input_tp_val / 20):.6f}"
+
+                if input_lra_val is not None and math.isfinite(input_lra_val):
+                    album_range = f"{input_lra_val:.2f}"
 
                 result: Dict = {
                     "replaygain_album_gain": album_gain,
@@ -838,7 +909,7 @@ class AcousticBrainzNG:
                     "replaygain_album_range": album_range
                 }
                 
-                album_r128_result = subprocess.run(
+                album_r128_proc = subprocess.run(
                     [ffmpeg_path, "-hide_banner", "-f", "concat", "-safe", "0", "-i", concat_file_path,
                      "-vn", "-af", "loudnorm=I=-23:print_format=json", "-f", "null", "-"],
                     capture_output=True,
@@ -847,28 +918,45 @@ class AcousticBrainzNG:
                     creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0
                 )
                 
-                if album_r128_result.returncode != 0:
+                if album_r128_proc.returncode != 0:
-                    log.error(f"FFmpeg failed for album R128 calculation on {len(album_track_files)} files with exit code {album_r128_result.returncode}")
+                    log.error(f"FFmpeg failed for album R128 calculation on {len(album_track_files)} files with exit code {album_r128_proc.returncode}")
                     log.error(f"Album files: {', '.join(album_track_files)}")
-                    if album_r128_result.stdout:
+                    if album_r128_proc.stdout:
-                        log.error(f"FFmpeg stdout: {album_r128_result.stdout}")
+                        log.error(f"FFmpeg stdout: {album_r128_proc.stdout}")
-                    if album_r128_result.stderr:
+                    if album_r128_proc.stderr:
-                        log.error(f"FFmpeg stderr: {album_r128_result.stderr}")
+                        log.error(f"FFmpeg stderr: {album_r128_proc.stderr}")
                     return result
 
+                album_r128_log = album_r128_proc.stderr or album_r128_proc.stdout
+                
+                album_r128_match = re.search(r'\{.*?\}', album_r128_log, re.S)
+                album_r128_matches = re.findall(r'\{.*?\}', album_r128_log, re.S) if not album_r128_match else None
+                album_r128_json_text = album_r128_match.group(0) if album_r128_match else (album_r128_matches[0] if album_r128_matches else None)
+                
                 r128_album_gain = None
                 
-                try:
+                r128_data: dict | None = None
-                    json_start = album_r128_result.stderr.find('{')
-                    if json_start != -1:
-                        json_str = album_r128_result.stderr[json_start:]
-                        json_end = json_str.find('}') + 1
-                        if json_end > 0:
-                            r128_data = json.loads(json_str[:json_end])
-                            r128_input_i = r128_data.get('input_i')
                 
-                            if r128_input_i and r128_input_i != "-inf":
+                if album_r128_json_text:
-                                r128_gain_db = -23 - float(r128_input_i)
+                    try:
+                        r128_data = json.loads(album_r128_json_text)
+                    except json.JSONDecodeError:
+                        if album_r128_matches:
+                            try:
+                                r128_data = json.loads(album_r128_matches[-1])
+                            except Exception:
+                                r128_data = None
+                
+                r128_input_i = r128_data.get('input_i') if r128_data else None
+                
+                try:
+                    if r128_input_i:
+                        r128_input_i_val = int(r128_input_i)
+                except (TypeError, ValueError):
+                    r128_input_i_val = None
+
+                if r128_input_i_val is not None and math.isfinite(r128_input_i_val):
+                    r128_gain_db = -23 - r128_input_i_val
                     r128_album_gain = int(round(r128_gain_db * 256))
                     
                     if r128_album_gain < -32768:
@@ -876,9 +964,6 @@ class AcousticBrainzNG:
                     elif r128_album_gain > 32767:
                         r128_album_gain = 32767
 
-                except (json.JSONDecodeError, ValueError, TypeError):
-                    pass
-
                 result["r128_album_gain"] = r128_album_gain
                 
                 return result
@@ -1362,8 +1447,8 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
         def update_concurrent_processes():
             concurrent_analyses = self.concurrent_analyses_input.value()
             musicnn_workers = self.musicnn_workers_input.value()
-            max_processes = concurrent_analyses + (concurrent_analyses * musicnn_workers)
+            max_processes = (2 * concurrent_analyses) + (concurrent_analyses * musicnn_workers)
-            breakdown = f"[{concurrent_analyses} gaia processes + ({concurrent_analyses} x {musicnn_workers}) MusicNN processes]"
+            breakdown = f"[(2 x {concurrent_analyses}) feature processes + ({concurrent_analyses} x {musicnn_workers}) MusicNN processes]"
             self.concurrent_processes_display.setText(f"{breakdown} = <span style='font-weight: bold;'>{max_processes}</span>")
 
         self.concurrent_analyses_input.valueChanged.connect(update_concurrent_processes)
@@ -1397,15 +1482,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
             lambda: (self._check_binaries(show_success=True), None)[1]
         )
 
-        # jq path
-        self.jq_path_input = QtWidgets.QLineEdit(self)
-        self.jq_path_input.setPlaceholderText("Path to jq")
-        jq_layout = self._create_path_input_layout(
-            self.jq_path_input,
-            lambda: self._browse_file(self.jq_path_input),
-            lambda: (self._check_binaries(show_success=True), None)[1]
-        )
-
         # Models path
         self.models_path_input = QtWidgets.QLineEdit(self)
         self.models_path_input.setPlaceholderText("Path to MusicNN models")
@@ -1425,8 +1501,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
 
         paths_layout.addWidget(QtWidgets.QLabel("FFmpeg", self))
         paths_layout.addLayout(ffmpeg_layout)
-        paths_layout.addWidget(QtWidgets.QLabel("jq", self))
-        paths_layout.addLayout(jq_layout)
         paths_layout.addWidget(QtWidgets.QLabel("Binaries", self))
         paths_layout.addLayout(binaries_layout)
         paths_layout.addWidget(QtWidgets.QLabel("Models", self))
@@ -1454,11 +1528,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
             QtWidgets.QMessageBox.warning(self, "Binaries", "Invalid or empty FFmpeg path.")
             return False
         
-        jq_path = self.jq_path_input.text()
-        if not jq_path or not os.path.exists(jq_path):
-            QtWidgets.QMessageBox.warning(self, "Binaries", "Invalid or empty jq path.")
-            return False
-        
         missing_binaries = []
         for binary in REQUIRED_BINARIES:
             binary_path = os.path.join(binaries_path, binary)
@@ -1481,20 +1550,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
             missing_binaries.append("FFmpeg (unable to execute)")
             log.error(f"Exception running FFmpeg version check: {e}")
         
-        try:
-            result = subprocess.run([jq_path, "--version"], capture_output=True, text=True, creationflags=subprocess.CREATE_NO_WINDOW if os.name == 'nt' else 0)
-            if result.returncode != 0 or not result.stdout.startswith("jq-"):
-                missing_binaries.append("jq (invalid executable)")
-                if result.returncode != 0:
-                    log.error(f"jq version check failed with exit code {result.returncode}")
-                    if result.stdout:
-                        log.error(f"jq stdout: {result.stdout}")
-                    if result.stderr:
-                        log.error(f"jq stderr: {result.stderr}")
-        except Exception as e:
-            missing_binaries.append("jq (unable to execute)")
-            log.error(f"Exception running jq version check: {e}")
-        
         if missing_binaries:
             message = f"Missing binaries:\n" + "\n".join(f"• {binary}" for binary in missing_binaries)
             QtWidgets.QMessageBox.warning(self, "Binaries", message)
@@ -1586,7 +1641,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
 
         self.binaries_path_input.setText(config.setting["acousticbrainz_ng_binaries_path"])
         self.ffmpeg_path_input.setText(config.setting["acousticbrainz_ng_ffmpeg_path"])
-        self.jq_path_input.setText(config.setting["acousticbrainz_ng_jq_path"])
         self.models_path_input.setText(config.setting["acousticbrainz_ng_models_path"])
         self.cache_path_input.setText(config.setting["acousticbrainz_ng_cache_path"])
     
@@ -1614,7 +1668,6 @@ class AcousticBrainzNGOptionsPage(OptionsPage):
 
         config.setting["acousticbrainz_ng_binaries_path"] = self.binaries_path_input.text()
         config.setting["acousticbrainz_ng_ffmpeg_path"] = self.ffmpeg_path_input.text()
-        config.setting["acousticbrainz_ng_jq_path"] = self.jq_path_input.text()
         config.setting["acousticbrainz_ng_models_path"] = self.models_path_input.text()
         config.setting["acousticbrainz_ng_cache_path"] = self.cache_path_input.text()
 

constants.py

@@ -18,7 +18,7 @@ External dependencies:
 </ul>
 <strong>This plugin is CPU heavy!</strong>
 """
-PLUGIN_VERSION = "1.1.1"
+PLUGIN_VERSION = "1.1.2"
 PLUGIN_API_VERSIONS = ["2.7", "2.8", "2.9", "2.10", "2.11", "2.12", "2.13"]
 PLUGIN_LICENSE = "GPL-2.0-or-later"
 PLUGIN_LICENSE_URL = "https://www.gnu.org/licenses/gpl-2.0.html"
@@ -46,8 +46,9 @@ OPTIONAL_MODELS: List[Tuple[str, str]] = [
 ]
 
 REQUIRED_BINARIES: List[str] = [
-    "streaming_extractor_music",
+    "streaming_rhythmextractor_multifeature",
     "streaming_musicnn_predict",
+    "streaming_key",
     "streaming_md5",
 ]
 
@@ -57,7 +58,6 @@ ENV['TF_ENABLE_ONEDNN_OPTS'] = "0"
 CONFIG_OPTIONS = [
     TextOption("setting", "acousticbrainz_ng_binaries_path", os.path.join(os.path.dirname(__file__), "bin")),
     TextOption("setting", "acousticbrainz_ng_ffmpeg_path", os.path.join(os.path.dirname(sys.executable), "ffmpeg" + (".exe" if os.name == "nt" else ""))),
-    TextOption("setting", "acousticbrainz_ng_jq_path", os.path.join(os.path.dirname(sys.executable), "jq" + (".exe" if os.name == "nt" else ""))),
     TextOption("setting", "acousticbrainz_ng_models_path", os.path.join(os.path.dirname(__file__), "models")),
     TextOption("setting", "acousticbrainz_ng_cache_path", os.path.join(os.path.dirname(__file__), "cache")),
     IntOption("setting", "acousticbrainz_ng_max_musicnn_workers", 4),
@@ -68,5 +68,3 @@ CONFIG_OPTIONS = [
     BoolOption("setting", "acousticbrainz_ng_calculate_replaygain", True),
     BoolOption("setting", "acousticbrainz_ng_save_fingerprint", True)
 ]
-
-GAIA_KEY_ALGORITHMS = ["edma", "krumhansl", "temperley"]