From a23201f7d01917fdfe52252a9fe574213d1d4481 Mon Sep 17 00:00:00 2001 From: faradox Date: Wed, 22 Nov 2023 08:53:02 +0100 Subject: [PATCH] rewrite to use nendo --- .dockerignore | 12 + .gitignore | 16 +- Dockerfile | 25 +- README.md | 172 +++--- __init__.py | 0 docs/images/polymath.png | Bin 0 -> 902595 bytes polymath.py | 1208 +++++++++++++++----------------------- requirements.txt | 22 +- run_docker.sh | 5 + setup.py | 16 + 10 files changed, 653 insertions(+), 823 deletions(-) create mode 100644 .dockerignore create mode 100644 __init__.py create mode 100644 docs/images/polymath.png create mode 100755 run_docker.sh create mode 100644 setup.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..f432270 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,12 @@ +input +nendo_library +library +processed +separated +polymath_library +polymath_input +polymath_output +.python-version +polymath.egg-info +__pycache__ +models \ No newline at end of file diff --git a/.gitignore b/.gitignore index e969c78..f432270 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,12 @@ -input/ -library/ -processed/ -separated/ \ No newline at end of file +input +nendo_library +library +processed +separated +polymath_library +polymath_input +polymath_output +.python-version +polymath.egg-info +__pycache__ +models \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 3db1f10..ce4b3eb 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,14 +1,21 @@ -FROM python:3.10-bullseye +FROM python:3.8-slim-buster -RUN apt update -RUN apt install -y rubberband-cli make automake gcc g++ python3-dev gfortran build-essential wget libsndfile1 ffmpeg +RUN apt-get update \ + && apt-get -y install rubberband-cli \ + libasound-dev portaudio19-dev \ + libportaudio2 libportaudiocpp0 git gcc \ + && rm -rf /var/lib/apt/lists/* -RUN pip install --upgrade pip +RUN pip install git+https://github.com/CPJKU/madmom.git@0551aa8 -COPY . /polymath WORKDIR /polymath +COPY . . +RUN pip install -r ./requirements.txt -RUN pip install -r requirements.txt - -RUN mkdir -p input processed separated library - +# fixes for some dependency conflicts +RUN pip uninstall -y soundfile +RUN pip install soundfile +RUN pip install soundfile==0.12.1 +RUN pip install numpy==1.22.4 +RUN pip uninstall -y essentia essentia-tensorflow +RUN pip install essentia-tensorflow diff --git a/README.md b/README.md index 4d6aabb..0dbead7 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,27 @@ - # Polymath -Polymath uses machine learning to convert any music library (*e.g from Hard-Drive or YouTube*) into a music production sample-library. The tool automatically separates songs into stems (*beats, bass, etc.*), quantizes them to the same tempo and beat-grid (*e.g. 120bpm*), analyzes musical structure (*e.g. verse, chorus, etc.*), key (*e.g C4, E3, etc.*) and other infos (*timbre, loudness, etc.*), and converts audio to midi. The result is a searchable sample library that streamlines the workflow for music producers, DJs, and ML audio developers. +Polymath uses machine learning to convert any music library (*e.g from Hard-Drive or YouTube*) into a music production sample-library. The tool automatically separates tracks into stems (_drums, bass, etc._), quantizes them to the same tempo and beat-grid (*e.g. 120bpm*), analyzes tempo, key (_e.g C4, E3, etc._) and other infos (*timbre, loudness, etc.*) and cuts loop out of them. The result is a searchable sample library that streamlines the workflow for music producers, DJs, and ML audio developers. + +Try it in colab: + +Open In Colab + -

Polymath

+![Polymath](docs/images/polymath.png) ## Use-cases -Polymath makes it effortless to combine elements from different songs to create unique new compositions: Simply grab a beat from a Funkadelic track, a bassline from a Tito Puente piece, and fitting horns from a Fela Kuti song, and seamlessly integrate them into your DAW in record time. Using Polymath's search capability to discover related tracks, it is a breeze to create a polished, hour-long mash-up DJ set. For ML developers, Polymath simplifies the process of creating a large music dataset, for training generative models, etc. + +Polymath makes it effortless to combine elements from different tracks to create unique new compositions: Simply grab a beat from a Funkadelic track, a bassline from a Tito Puente piece, and fitting horns from a Fela Kuti song, and seamlessly integrate them into your DAW in record time. Using Polymath's search capability to discover related tracks, it is a breeze to create a polished, hour-long mash-up DJ set. For ML developers, Polymath simplifies the process of creating a large music dataset, for training generative models, etc. ## How does it work? -- Music Source Separation is performed with the [Demucs](https://github.com/facebookresearch/demucs) neural network -- Music Structure Segmentation/Labeling is performed with the [sf_segmenter](https://github.com/wayne391/sf_segmenter) neural network -- Music Pitch Tracking and Key Detection are performed with [Crepe](https://github.com/marl/crepe) neural network -- Music to MIDI transcription is performed with [Basic Pitch](https://github.com/spotify/basic-pitch) neural network -- Music Quantization and Alignment are performed with [pyrubberband](https://github.com/bmcfee/pyrubberband) -- Music Info retrieval and processing is performed with [librosa](https://github.com/librosa/librosa) + +- Import tracks from youtube or directly from your google drive +- Process selected (or all) tracks with a configurable selection of nendo plugins: + - Apply the [classification plugin](https://github.com/okio-ai/nendo-plugin-classify-core) to compute _volume_, _tempo_ (bpm), _key_, _intensity_, _frequency_, and _loudness_ for each track + - Apply the [stemification plugin](https://github.com/okio-ai/nendo-plugin-stemify-demucs) to separate each track into four source signals: _vocals_, _drum_, _bass_, and _other_ + - Apply the [quantization plugin](https://github.com/okio-ai/nendo-plugin-quantize-core) to quantize each track to a specified target _bpm_ + - Apply the [loopification plugin](https://github.com/okio-ai/nendo-plugin-loopify) to automatically detect and extract loops from each sample +- Export the results of the processing with informative file names to your google drive in _wav_, _mp3_ or _ogg_ format. ## Community @@ -22,23 +29,31 @@ Join the Polymath Community on [Discord](https://discord.gg/gaZMZKzScj) ## Requirements -You need to have the following software installed on your system: +**Polymath requires Python version 3.8, 3.9 or 3.10.** + +> It is recommended to use a [virtual environment](https://docs.python.org/3/library/venv.html), in order to avoid dependency conflicts. You can use your favorite virtual environment management system, like [conda](https://docs.conda.io/en/latest/), [poetry](https://python-poetry.org/), or [pyenv](https://github.com/pyenv/pyenv) for example. + +Furthermore, the following software packages need to be installed in your system: -- ``ffmpeg`` +- **Ubuntu**: `sudo apt-get install ffmpeg libsndfile1 libportaudio2 rubberband-cli libmpg123-dev` +- **Mac OS**: `brew install ffmpeg libsndfile portaudio rubberband mpg123` +- **Windows** + + > Windows support is currently under development. For the time being, we highly recommend using [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/install) and then following the linux instructions. ## Installation -You need python version `>=3.7` and `<=3.10`. From your terminal run: +You need python version `>=3.8` and `<=3.10`. From your terminal run: + ```bash +pip install git+https://github.com/CPJKU/madmom.git@0551aa8 git clone https://github.com/samim23/polymath cd polymath pip install -r requirements.txt +pip uninstall -y essentia essentia-tensorflow && pip install essentia-tensorflow ``` -If you run into an issue with basic-pitch while trying to run Polymath, run this command after your installation: -```bash -pip install git+https://github.com/spotify/basic-pitch.git -``` +The last line is a fix that's needed to avoid a dependency conflict among the plugins. ## GPU support @@ -54,114 +69,119 @@ docker build -t polymath ./ In order to exchange input and output files between your hosts system and the polymath docker container, you need to create the following four directories: -- `./input` -- `./library` -- `./processed` -- `./separated` +- `./polymath_input` +- `./polymath_library` +- `./polymath_output` +- `./models` + +E.g. run `mkdir -p ./polymath_input ./polymath_library ./polymath_output ./models`. -Now put any files you want to process with polymath into the `input` folder. +Now put any files you want to process with polymath into the `polymath_input` folder. Then you can run polymath through docker by using the `docker run` command and pass any arguments that you would originally pass to the python command, e.g. if you are in a linux OS call: ```bash docker run \ - -v "$(pwd)"/processed:/polymath/processed \ - -v "$(pwd)"/separated:/polymath/separated \ - -v "$(pwd)"/library:/polymath/library \ - -v "$(pwd)"/input:/polymath/input \ - polymath python /polymath/polymath.py -a ./input/song1.wav + -v "$(pwd)"/models:/polymath/models \ + -v "$(pwd)"/polymath_input:/polymath/polymath_input \ + -v "$(pwd)"/polymath_library:/polymath/polymath_library \ + -v "$(pwd)"/polymath_output:/polymath/polymath_output \ + polymath \ + python polymath.py -i ./polymath_input/song1.wav -p -e ``` ## Run Polymath -### 1. Add songs to the Polymath Library +To print the help for the python command line arguments: + +```bash +python polymath.py -h +``` + +### 1. Add tracks to the Polymath Library ##### Add YouTube video to library (auto-download) + ```bash -python polymath.py -a n6DAqMFe97E +python polymath.py -i n6DAqMFe97E ``` + ##### Add audio file (wav or mp3) + ```bash -python polymath.py -a /path/to/audiolib/song.wav +python polymath.py -i /path/to/audiolib/song.wav ``` + ##### Add multiple files at once -```bash -python polymath.py -a n6DAqMFe97E,eaPzCHEQExs,RijB8wnJCN0 -python polymath.py -a /path/to/audiolib/song1.wav,/path/to/audiolib/song2.wav -python polymath.py -a /path/to/audiolib/ -``` -Songs are automatically analyzed once which takes some time. Once in the database, they can be access rapidly. The database is stored in the folder "/library/database.p". To reset everything, simply delete it. -### 2. Quantize songs in the Polymath Library -##### Quantize a specific songs in the library to tempo 120 BPM (-q = database audio file ID, -t = tempo in BPM) -```bash -python polymath.py -q n6DAqMFe97E -t 120 -``` -##### Quantize all songs in the library to tempo 120 BPM ```bash -python polymath.py -q all -t 120 +python polymath.py -i n6DAqMFe97E,eaPzCHEQExs,RijB8wnJCN0 +python polymath.py -i /path/to/audiolib/song1.wav,/path/to/audiolib/song2.wav +python polymath.py -i /path/to/audiolib/ +# you can even mix imports: +python polymath.py -i /path/to/audiolib/,n6DAqMFe97E,/path/to/song2.wav ``` -##### Quantize a specific songs in the library to the tempo of the song (-k) -```bash -python polymath.py -q n6DAqMFe97E -k -``` -Songs are automatically quantized to the same tempo and beat-grid and saved to the folder “/processed”. -### 3. Search for similar songs in the Polymath Library -##### Search for 10 similar songs based on a specific songs in the library (-s = database audio file ID, -sa = results amount) +Once in the database, they can be searched through, processed and exported. The database is stored by default in the folder "./polymath_library". To change the library folder use the `--library_path` console argument. To reset everything, simply delete that directory. + +### 2. Quantize tracks in the Polymath Library + +##### Find a specific song in the library and quantize it to tempo 120 BPM (-f = find ID in library, -q = quantize to tempo in BPM) + ```bash -python polymath.py -s n6DAqMFe97E -sa 10 +python polymath.py -f n6DAqMFe97E -q 120 ``` -##### Search for similar songs based on a specific songs in the library and quantize all of them to tempo 120 BPM + +##### Quantize all tracks in the library to tempo 120 BPM + ```bash -python polymath.py -s n6DAqMFe97E -sa 10 -q all -t 120 +python polymath.py -q 120 ``` -##### Include BPM as search criteria (-st) + +### 3. Search for specific tracks in the Polymath Library + +##### Find tracks with specific search keys in the library and export them + ```bash -python polymath.py -s n6DAqMFe97E -sa 10 -q all -t 120 -st -k +python polymath.py -f n6DAqMFe97E,my_song.mp3 -e ``` -Similar songs are automatically found and optionally quantized and saved to the folder "/processed". This makes it easy to create for example an hour long mix of songs that perfectly match one after the other. -### 4. Convert Audio to MIDI -##### Convert all processed audio files and stems to MIDI (-m) +The default export directory is `./polymath_output`. To specify a different directory, use the `-o /path/to/my/output/dir` flag. + +##### Find tracks in specific BPM range as search criteria (-bmin and -bmax) and also export loops (-fl) + ```bash -python polymath.py -a n6DAqMFe97E -q all -t 120 -m +python polymath.py -bmin 80 -bmax 100 -fl -e ``` -Generated Midi Files are currently always 120BPM and need to be time adjusted in your DAW. This will be resolved [soon](https://github.com/spotify/basic-pitch/issues/40). The current Audio2Midi model gives mixed results with drums/percussion. This will be resolved with additional audio2midi model options in the future. - ## Audio Features ### Extracted Stems -The Demucs Neural Net has settings that can be adjusted in the python file + +Stems are extracted with the [nendo stemify plugin](https://github.com/okio-ai/nendo_plugin_stemify_demucs/). Extracted stem types are: + ```bash - bass - drum -- guitare -- other -- piano - vocals +- other ``` + ### Extracted Features -The audio feature extractors have settings that can be adjusted in the python file + +Music Information Retrieval features are computed using the [nendo classify plugin](https://github.com/okio-ai/nendo_plugin_classify_core/). Extracted features are: + ```bash - tempo - duration -- timbre -- timbre_frames -- pitch -- pitch_frames - intensity -- intensity_frames -- volume - avg_volume - loudness -- beats -- segments_boundaries -- segments_labels -- frequency_frames - frequency - key ``` ## License + Polymath is released under the MIT license as found in the [LICENSE](https://github.com/samim23/polymath/blob/main/LICENSE) file. + +As for [nendo core](https://github.com/okio-ai/nendo) and the [plugins used in polymath](#how-does-it-work), see their respective repositories for information about their license. \ No newline at end of file diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/docs/images/polymath.png b/docs/images/polymath.png new file mode 100644 index 0000000000000000000000000000000000000000..e9c9d8aea1caeba83e9a39b0cf890b55a5709f31 GIT binary patch literal 902595 zcmeFacUY6z);Am#0TqFW0!nBB1rY=kCG;XiL5dA2(v@Bey@R3>S`ZOIsY(+WK%{rn zKrBd=UV@w_s$aW3ZqNi6v 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zwH{ttE)cGxDLV<;gdBjM;~2LX8xVQOIyq+2o9Z^AqPL$+o_c|-_!&4;(gX|RTMc#f z0}wxHbyddD&=7nIuL)4JJEzm{*Q8e&21u=uiHV8MHDIxU28-Pg^Wmd!!4@sb;aOf1 zpWGMZBepG;r%PuD{BTM!-@6sZ!k+A`tWZG4v_7uDs+tAe5r3}D#BD?u7<+K9fqXlc zr^Kh6m8ZYTeHSqh0|l0li&N?1LKnCKSQ+zy#)P@p$ac1;X>uX1XJLKaD00WTJT+_U z9RJLbMJH9zGjS&btgQ|St0lKy0+w3h+ZK?B0ru7I#k7t5zzykNJ0JV6`|ters-2|P zV+%}jAcue>s>1m0RM;h2pNvH=q@0|(H@NlE;=BB3IB27#*`S`C(I56gtPw`>&Zh@e zzjty6e$ zsK^UOKr^V6_G!eZTO7Kluc$~dIhFf%l+93!(%zT4Pd&l)P5RNrAJC|8mLHo;$6b8M z8jz8)M?H|)O+2P>LSICywRlTq9FRRAJxZ*BuZpgg zNabqu*IAxn9|67NfNzh3+oI^8P{da|Z*O@w22`2pcA+^X*bO`jAoq4XI!kNfuJnum zT2E#V>QYbDE0g{Z8@BzE+{1ljA=w1aK0T*-Cdz zc`5o50BLY?aV3JPU!PX$8iEc4B~}hfD`!zaU;||cxo}Xd{wf#@OUY<9EOP1!QR7sA z 0: - videos = audio_process(filesToProcess, videos) - return videos - -def audio_process(vids, videos): - for vid in vids: - print('------ process audio',vid) - # extract file name - audioname = vid.split("/")[-1] - audioname, _ = audioname.split(".") - - # generate a unique ID based on file path and name - hash_object = hashlib.sha256(vid.encode()) - audioid = hash_object.hexdigest() - audioid = f"{audioname}_{audioid}" - - # check if id already in db - process_audio = True - for video in videos: - if video.id == audioid: - print("already in db",vid) - process_audio = False - break - - # check if is mp3 and convert it to wav - if vid.endswith(".mp3"): - # convert mp3 to wav and save it - print('converting mp3 to wav:', vid) - y, sr = librosa.load(path=vid, sr=None, mono=False) - path = os.path.join(os.getcwd(), 'library', audioid+'.wav') - # resample to 44100k if required - if sr != 44100: - print('converting audio file to 44100:', vid) - y = librosa.resample(y, orig_sr=sr, target_sr=44100) - sf.write(path, np.ravel(y), 44100) - vid = path - - # check if is wav and copy it to local folder - elif vid.endswith(".wav"): - path1 = vid - path2 = os.path.join(os.getcwd(), 'library', audioid+'.wav') - y, sr = librosa.load(path=vid, sr=None, mono=False) - if sr != 44100: - print('converting audio file to 44100:', vid) - y = librosa.resample(y, orig_sr=sr, target_sr=44100) - sf.write(path2, y, 44100) - else: - shutil.copy2(path1, path2) - vid = path2 - - # analyse videos and save to disk - if process_audio: - video = Video(audioname,'',vid) - video.id = audioid - video.url = vid - videos.append(video) - write_library(videos) - print("Finished procesing files:",len(videos)) - - return videos - -################## AUDIO FEATURES ################## - -def root_mean_square(data): - return float(np.sqrt(np.mean(np.square(data)))) - -def loudness_of(data): - return root_mean_square(data) - -def normalized(list): - """Given an audio buffer, return it with the loudest value scaled to 1.0""" - return list.astype(np.float32) / float(np.amax(np.abs(list))) -neg80point8db = 0.00009120108393559096 -bit_depth = 16 -default_silence_threshold = (neg80point8db * (2 ** (bit_depth - 1))) * 4 -def start_of(list, threshold=default_silence_threshold, samples_before=1): - if int(threshold) != threshold: - threshold = threshold * float(2 ** (bit_depth - 1)) - index = np.argmax(np.absolute(list) > threshold) - if index > (samples_before - 1): - return index - samples_before - else: - return 0 - -def end_of(list, threshold=default_silence_threshold, samples_after=1): - if int(threshold) != threshold: - threshold = threshold * float(2 ** (bit_depth - 1)) - rev_index = np.argmax( - np.flipud(np.absolute(list)) > threshold - ) - if rev_index > (samples_after - 1): - return len(list) - (rev_index - samples_after) - else: - return len(list) +def import_youtube(nendo, yt_id): + """Import the audio from the given youtube ID into the library. + + Args: + nendo (Nendo): The nendo instance to use. + yt_id (str): ID of the youtube video. + + Returns: + NendoTrack: The imported track. + """ + # check if id already in db + db_tracks = nendo.library.find_tracks(value=yt_id) + if len(db_tracks) > 0: + print(f"Track with youtube id {yt_id} already exists in library. Skipping.") + return None + + # analyse videos and save to disk + url = f"https://www.youtube.com/watch?v={yt_id}" + with tempfile.TemporaryDirectory() as temp_dir: + ydl_opts = { + "quiet": True, + "outtmpl": temp_dir + "/%(id)s.%(ext)s", + "format": "bestaudio/best", + "postprocessors": [ + { + "key": "FFmpegExtractAudio", + "preferredcodec": "vorbis", + "preferredquality": "192", + }, + ], + } + with YoutubeDL(ydl_opts) as ydl: + result = ydl.extract_info(url, download=True) + video = result["entries"][0] if "entries" in result else result + return nendo.library.add_track( + file_path=f"{temp_dir}/{video['id']}.ogg", + meta={ + "original_info": video["id"], + }, + ) -def trim_data( - data, - start_threshold=default_silence_threshold, - end_threshold=default_silence_threshold +def input_tracks(nendo, input_string): + """Import a list of tracks from youtube and local FS. + + Args: + nendo (Nendo): The Nendo instance to use. + input_string (str): Comma-separated list of items to import into the + polymath library. + """ + input_items = input_string.split(",") + for item in input_items: + if os.path.isdir(item): + nendo.add_tracks(path=item) + elif os.path.isfile(item): + nendo.add_track(file_path=item) + else: + import_youtube(nendo, item) + print(f"Added {item}") + +def process_tracks( + nendo, + tracks, + analyze, + stemify, + quantize, + quantize_to_bpm, + loopify, + n_loops, + beats_per_loop, ): - start = start_of(data, start_threshold) - end = end_of(data, end_threshold) - - return data[start:end] - -def load_and_trim(file): - y, rate = librosa.load(file, mono=True) - y = normalized(y) - trimmed = trim_data(y) - return trimmed, rate - -def get_loudness(file): - loudness = -1 - try: - audio, rate = load_and_trim(file) - loudness = loudness_of(audio) - except Exception as e: - sys.stderr.write(f"Failed to run on {file}: {e}\n") - return loudness - -def get_volume(file): - volume = -1 - avg_volume = -1 - try: - audio, rate = load_and_trim(file) - volume = librosa.feature.rms(y=audio)[0] - avg_volume = np.mean(volume) - loudness = loudness_of(audio) - except Exception as e: - sys.stderr.write(f"Failed to get Volume and Loudness on {file}: {e}\n") - return volume, avg_volume, loudness - -def get_key(freq): - A4 = 440 - C0 = A4*pow(2, -4.75) - name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] - h = round(12*log2(freq/C0)) - octave = h // 12 - n = h % 12 - return name[n] + str(octave) - -def get_average_pitch(pitch): - pitches = [] - confidences_thresh = 0.8 - i = 0 - while i < len(pitch): - if(pitch[i][2] > confidences_thresh): - pitches.append(pitch[i][1]) - i += 1 - if len(pitches) > 0: - average_frequency = np.array(pitches).mean() - average_key = get_key(average_frequency) - else: - average_frequency = 0 - average_key = "A0" - return average_frequency,average_key - -def get_intensity(y, sr, beats): - # Beat-synchronous Loudness - Intensity - CQT = librosa.cqt(y, sr=sr, fmin=librosa.note_to_hz('A1')) - freqs = librosa.cqt_frequencies(CQT.shape[0], fmin=librosa.note_to_hz('A1')) - perceptual_CQT = librosa.perceptual_weighting(CQT**2, freqs, ref=np.max) - CQT_sync = librosa.util.sync(perceptual_CQT, beats, aggregate=np.median) - return CQT_sync - -def get_pitch(y_harmonic, sr, beats): - # Chromagram - C = librosa.feature.chroma_cqt(y=y_harmonic, sr=sr) - # Beat-synchronous Chroma - Pitch - C_sync = librosa.util.sync(C, beats, aggregate=np.median) - return C_sync - -def get_timbre(y, sr, beats): - # Mel spectogram - S = librosa.feature.melspectrogram(y, sr=sr, n_mels=128) - log_S = librosa.power_to_db(S, ref=np.max) - # MFCC - Timbre - mfcc = librosa.feature.mfcc(S=log_S, n_mfcc=13) - delta_mfcc = librosa.feature.delta(mfcc) - delta2_mfcc = librosa.feature.delta(mfcc, order=2) - M = np.vstack([mfcc, delta_mfcc, delta2_mfcc]) - # Beat-synchronous MFCC - Timbre - M_sync = librosa.util.sync(M, beats) - return M_sync - -def get_segments(audio_file): - segmenter = Segmenter() - boundaries, labs = segmenter.proc_audio(audio_file) - return boundaries,labs - -def get_pitch_dnn(audio_file): - # DNN Pitch Detection - pitch = [] - audio, sr = librosa.load(audio_file) - time, frequency, confidence, activation = crepe.predict(audio, sr, model_capacity="tiny", viterbi=True, center=True, step_size=10, verbose=1) # tiny|small|medium|large|full - i = 0 - while i < len(time): - pitch.append([time[i],frequency[i],confidence[i]]) - i += 1 - return pitch - -def stemsplit(destination, demucsmodel): - subprocess.run(["demucs", destination, "-n", demucsmodel]) # '--mp3' - -def extractMIDI(audio_paths, output_dir): - print('- Extract Midi') - save_midi = True - sonify_midi = False - save_model_outputs = False - save_notes = False - - predict_and_save(audio_path_list=audio_paths, - output_directory=output_dir, - save_midi=save_midi, - sonify_midi=sonify_midi, - save_model_outputs=save_model_outputs, - save_notes=save_notes) - - -def quantizeAudio(vid, bpm=120, keepOriginalBpm = False, pitchShiftFirst = False, extractMidi = False): - print("Quantize Audio: Target BPM", bpm, - "-- id:",vid.id, - "bpm:",round(vid.audio_features["tempo"],2), - "frequency:",round(vid.audio_features['frequency'],2), - "key:",vid.audio_features['key'], - "timbre:",round(vid.audio_features['timbre'],2), - "name:",vid.name, - 'keepOriginalBpm:', keepOriginalBpm + """Process the given list of NendoTracks using Nendo plugins. + + Args: + nendo (Nendo): The Nendo instance to use. + tracks (List[NendoTrack]): List of tracks to process. + analyze (bool): Flag determining whether to analyze as part of processing. + stemify (bool): Flag determining whether to stemify as part of processing. + quantize (bool): Flag determining whether to quantize as part of processing. + quantize_to_bpm (int): Target bpm for quantization. + loopify (bool): Flag determining whether to loopify as part of processing. + n_loops (int): Number of loops to extract. + beats_per_loop (int): Beats per loop to extract. + """ + n = 0 + for track in tracks: + original_title = track.meta["title"] + print(f"Processing track {1}/{len(tracks)}: {original_title}") + duration = round(librosa.get_duration(y=track.signal, sr=track.sr), 3) + if (analyze is True and ( + len( + track.get_plugin_data( + plugin_name="nendo_plugin_classify_core", + ), + ) == 0 or nendo.config.replace_plugin_data is True) + ): + print("Analyzing...") + track.process("nendo_plugin_classify_core") + # analysis_data = track.get_plugin_data( + # plugin_name="nendo_plugin_classify_core", + # ) + stems = track + if (stemify is True and + track.track_type != "stem" and + "has_stems" not in track.resource.meta): + print("Stemifying...") + stems = track.process("nendo_plugin_stemify_demucs") + track.set_meta({"has_stems": True }) + for stem in stems: + stem_type = stem.get_meta("stem_type") + stem.meta = dict(track.meta) + stem.set_meta( + { + "title": f"{original_title} - {stem_type} stem", + "stem_type": stem_type, + "duration": duration, + }, + ) + quantized = stems + if quantize is True: + print("Quantizing...") + quantized = stems.process( + "nendo_plugin_quantize_core", + bpm=quantize_to_bpm, + ) + if type(quantized) == NendoTrack: # is a single track + if not quantized.has_related_track(track_id=track.id, direction="from"): + quantized.relate_to_track( + track_id=track.id, + relationship_type="quantized", + ) + quantized.meta = dict(track.meta) + duration = round(librosa.get_duration(y=quantized.signal, sr=quantized.sr), 3) + quantized.set_meta( + { + "title": f"{original_title} - ({quantize_to_bpm} bpm)", + "duration": duration, + }, + ) + else: # is a collection + for j, qt in enumerate(quantized): + if not qt.has_related_track(track_id=track.id, direction="from"): + qt.relate_to_track( + track_id=track.id, + relationship_type="quantized", + ) + qt.meta = dict(track.meta) + duration = round(librosa.get_duration(y=qt.signal, sr=qt.sr), 3) + if stems[j].track_type == "stem": + qt.set_meta( + { + "title": ( + f"{original_title} - " + f"{stems[j].meta['stem_type']} " + f"({quantize_to_bpm} bpm)" + ), + "stem_type": stems[j].meta["stem_type"], + "duration": duration, + }, + ) + else: + qt.set_meta( + { + "title": f"{original_title} ({quantize_to_bpm} bpm)", + "duration": duration, + }, + ) + loopified = quantized + if loopify is True: + print("Loopifying...") + loopified = [] + if type(quantized) == NendoTrack: + quantized = [quantized] + for qt in quantized: + qt_loops = qt.process( + "nendo_plugin_loopify", + n_loops=n_loops, + beats_per_loop=beats_per_loop, + ) + loopified += qt_loops + num_loop = 1 + for lp in qt_loops: + if not lp.has_related_track(track_id=track.id, direction="from"): + lp.relate_to_track( + track_id=track.id, + relationship_type="loop", + ) + stem_type = qt.meta["stem_type"] if qt.has_meta("stem_type") else "" + qt_info = ( + f" ({quantize_to_bpm} bpm)" + if qt.track_type == "quantized" + else "" + ) + lp.meta = dict(track.meta) + duration = round(librosa.get_duration(y=lp.signal, sr=lp.sr), 3) + lp.set_meta( + { + "title": f"{original_title} - {stem_type} loop {num_loop} {qt_info}", + "duration": duration, + }, + ) + num_loop += 1 + n = n+1 + print(f"Track {n}/{len(tracks)} Done.\n") + print("Processing completed. " + f"The library now contains {len(nendo.library)} tracks.") + +def export_tracks(nendo, tracks, output_folder, export_format): + """Export the given tracks to the given output folder in the given format. + + Args: + nendo (Nendo): The Nendo instance to use. + tracks (List[NendoTrack]): List of track to export. + output_folder (str): Output path. + export_format (str): Output format. + """ + if os.path.exists(output_folder) is False: + os.mkdir(output_folder) + + print(f"Exporting {len(tracks)} tracks") + for found_track in tracks: + bpm = "" + bpm_pd = found_track.get_plugin_data( + key="tempo", ) - - # load audio file - y, sr = librosa.load(vid.audio, sr=None) - - # Keep Original Song BPM - if keepOriginalBpm: - bpm = float(vid.audio_features['tempo']) - print('Keep original audio file BPM:', vid.audio_features['tempo']) - # Pitch Shift audio file to desired BPM first - elif pitchShiftFirst: # WORK IN PROGRESS - print('Pitch Shifting audio to desired BPM', bpm) - # Desired tempo in bpm - original_tempo = vid.audio_features['tempo'] - speed_factor = bpm / original_tempo - # Resample the audio to adjust the sample rate accordingly - sr_stretched = int(sr / speed_factor) - y = librosa.resample(y=y, orig_sr=sr, target_sr=sr_stretched) #, res_type='linear' - y = librosa.resample(y, orig_sr=sr, target_sr=44100) - - # extract beat - y_harmonic, y_percussive = librosa.effects.hpss(y) - tempo, beats = librosa.beat.beat_track(sr=sr, onset_envelope=librosa.onset.onset_strength(y=y_percussive, sr=sr), trim=False) - beat_frames = librosa.frames_to_samples(beats) - - # generate metronome - fixed_beat_times = [] - for i in range(len(beat_frames)): - fixed_beat_times.append(i * 120 / bpm) - fixed_beat_frames = librosa.time_to_samples(fixed_beat_times) - - # construct time map - time_map = [] - for i in range(len(beat_frames)): - new_member = (beat_frames[i], fixed_beat_frames[i]) - time_map.append(new_member) - - # add ending to time map - original_length = len(y+1) - orig_end_diff = original_length - time_map[i][0] - new_ending = int(round(time_map[i][1] + orig_end_diff * (tempo / bpm))) - new_member = (original_length, new_ending) - time_map.append(new_member) - - # time strech audio - print('- Quantize Audio: source') - strechedaudio = pyrb.timemap_stretch(y, sr, time_map) - - path_suffix = ( - f"Key {vid.audio_features['key']} - " - f"Freq {round(vid.audio_features['frequency'], 2)} - " - f"Timbre {round(vid.audio_features['timbre'], 2)} - " - f"BPM Original {int(vid.audio_features['tempo'])} - " - f"BPM {bpm}" + if not isinstance(bpm_pd, list) and len(bpm_pd) != 0: + bpm = f"_{int(float(bpm_pd))}bpm" + stem = ( + f"_{found_track.get_meta('stem_type')}" if + found_track.has_meta("stem_type") else + "" + ) + track_type = ( + f"_{found_track.track_type}" if + found_track.track_type != "track" else + "" + ) + alphabet = string.ascii_lowercase + string.digits + rnd = "".join(random.choices(alphabet, k=6)) # noqa: S311 + file_path = ( + f"{output_folder}/{found_track.meta['title']}" + f"{stem}{track_type}{bpm}" + f"_{rnd}.{export_format}" + ) + exported_path = nendo.library.export_track( + track_id=found_track.id, + file_path=file_path, + ) + print(f"Exported track {exported_path}") + +def parse_args() -> argparse.Namespace: # noqa: D103 + parser = argparse.ArgumentParser() + parser.add_argument( + "-i", + "--input", + type=str, + required=False, + help=("Comma-separated list of tracks to add to the polymath library." + "Tracks can be either youtube IDs, file paths or directories." + ), ) - path_prefix = ( - f"{vid.id} - {vid.name}" + parser.add_argument( + "-p", + "--process", + required=False, + action="store_true", + help=( + "Flag that enables all processing steps " + "(use -q aswell to specify target bpm)." + ), + ) + parser.add_argument( + "-a", + "--analyze", + required=False, + action="store_true", + help="Flag to enable running the analysis plugin.", + ) + parser.add_argument( + "-s", + "--stemify", + required=False, + action="store_true", + help="Flag to enable running the stemify plugin.", + ) + parser.add_argument( + "-q", + "--quantize", + required=False, + type=int, + help="Quantize tracks to given bpm (default is 120).", + ) + parser.add_argument( + "-l", + "--loopify", + required=False, + action="store_true", + help="Flag to enable running the loopify plugin.", + ) + parser.add_argument( + "-ln", + "--loop_num", + type=int, + default=1, + help="Number of loops to extract (requires -l, default is 1).", + ) + parser.add_argument( + "-lb", + "--loop_beats", + type=int, + default=8, + help="Number of beats per loop (requires -l, default is 8).", + ) + parser.add_argument( + "-f", + "--find", + required=False, + type=str, + help=( + "Find specific tracks to apply polymath. " + "Omit to work with all tracks." + ), + ) + parser.add_argument( + "-bmin", + "--bpm_min", + required=False, + type=int, + help="Find tracks with a minimum bpm provided by this paramter.", + ) + parser.add_argument( + "-bmax", + "--bpm_max", + required=False, + type=int, + help="Find tracks with a maximum bpm provided by this paramter.", + ) + parser.add_argument( + "-fs", + "--find_stems", + required=False, + action="store_true", + help="Flag to enable finding stems.", + ) + parser.add_argument( + "-st", + "--stem_type", + choices=["any", "vocals", "drums", "bass", "other"], + default="any", + help="Flag to enable searching for stems.", + ) + parser.add_argument( + "-fl", + "--find_loops", + required=False, + action="store_true", + help="Flag to enable finding loops.", + ) + parser.add_argument( + "-e", + "--export", + required=False, + action="store_true", + help="Flag to enable exporting of tracks.", + ) + parser.add_argument( + "-o", + "--output_path", + type=str, + default="polymath_output", + help="Output path for file export.", + ) + parser.add_argument( + "-of", + "--output_format", + choices=["wav", "mp3", "ogg"], + default="wav", + help="Output path for file export.", + ) + parser.add_argument( + "-lp", + "--library_path", + default="polymath_library", + type=str, + help="Path to the polymath library directory.", + ) + return parser.parse_args() + + +def main(): # noqa: D103 + args = parse_args() + + nendo = Nendo( + config=NendoConfig( + log_level="error", + library_path=args.library_path, + skip_duplicate=True, + plugins=[ + "nendo_plugin_classify_core", + "nendo_plugin_quantize_core", + "nendo_plugin_stemify_demucs", + "nendo_plugin_loopify", + ], + ), ) - audiofilepaths = [] - # save audio to disk - path = os.path.join(os.getcwd(), 'processed', path_prefix + " - " + path_suffix +'.wav') - sf.write(path, strechedaudio, sr) - audiofilepaths.append(path) - - # process stems - stems = ['bass', 'drums', 'guitar', 'other', 'piano', 'vocals'] - for stem in stems: - path = os.path.join(os.getcwd(), 'separated', 'htdemucs_6s', vid.id, stem +'.wav') - print(f"- Quantize Audio: {stem}") - y, sr = librosa.load(path, sr=None) - strechedaudio = pyrb.timemap_stretch(y, sr, time_map) - # save stems to disk - path = os.path.join(os.getcwd(), 'processed', path_prefix + " - Stem " + stem + " - " + path_suffix +'.wav') - sf.write(path, strechedaudio, sr) - audiofilepaths.append(path) - - # metronome click (optinal) - click = False - if click: - clicks_audio = librosa.clicks(times=fixed_beat_times, sr=sr) - print(len(clicks_audio), len(strechedaudio)) - clicks_audio = clicks_audio[:len(strechedaudio)] - path = os.path.join(os.getcwd(), 'processed', vid.id + '- click.wav') - sf.write(path, clicks_audio, sr) - - if extractMidi: - output_dir = os.path.join(os.getcwd(), 'processed') - extractMIDI(audiofilepaths, output_dir) - - -def get_audio_features(file,file_id,extractMidi = False): - print("------------------------------ get_audio_features:",file_id,"------------------------------") - print('1/8 segementation') - segments_boundaries,segments_labels = get_segments(file) - - print('2/8 pitch tracking') - frequency_frames = get_pitch_dnn(file) - average_frequency,average_key = get_average_pitch(frequency_frames) - - print('3/8 load sample') - y, sr = librosa.load(file, sr=None) - song_duration = librosa.get_duration(y=y, sr=sr) - - print('4/8 sample separation') - y_harmonic, y_percussive = librosa.effects.hpss(y) - - print('5/8 beat tracking') - tempo, beats = librosa.beat.beat_track(sr=sr, onset_envelope=librosa.onset.onset_strength(y=y_percussive, sr=sr), trim=False) - - print('6/8 feature extraction') - CQT_sync = get_intensity(y, sr, beats) - C_sync = get_pitch(y_harmonic, sr, beats) - M_sync = get_timbre(y, sr, beats) - volume, avg_volume, loudness = get_volume(file) - - print('7/8 feature aggregation') - intensity_frames = np.matrix(CQT_sync).getT() - pitch_frames = np.matrix(C_sync).getT() - timbre_frames = np.matrix(M_sync).getT() - - if cuda.is_available(): - print('Cleaning up GPU memory') - device = cuda.get_current_device() - device.reset() - - print('8/8 split stems') - stemsplit(file, 'htdemucs_6s') - - if extractMidi: - audiofilepaths = [] - stems = ['bass', 'drums', 'guitar', 'other', 'piano', 'vocals'] - for stem in stems: - path = os.path.join(os.getcwd(), 'separated', 'htdemucs_6s', file_id, stem +'.wav') - audiofilepaths.append(path) - output_dir = os.path.join(os.getcwd(), 'separated', 'htdemucs_6s', file_id) - extractMIDI(audiofilepaths, output_dir) - - audio_features = { - "id":file_id, - "tempo":tempo, - "duration":song_duration, - "timbre":np.mean(timbre_frames), - "timbre_frames":timbre_frames, - "pitch":np.mean(pitch_frames), - "pitch_frames":pitch_frames, - "intensity":np.mean(intensity_frames), - "intensity_frames":intensity_frames, - "volume": volume, - "avg_volume": avg_volume, - "loudness": loudness, - "beats":librosa.frames_to_time(beats, sr=sr), - "segments_boundaries":segments_boundaries, - "segments_labels":segments_labels, - "frequency_frames":frequency_frames, - "frequency":average_frequency, - "key":average_key - } - return audio_features - -################## SEARCH NEAREST AUDIO ################## - -previous_list = [] - -def get_nearest(query,videos,querybpm, searchforbpm): - global previous_list - # print("Search: query:", query.name, '- Incl. BPM in search:', searchforbpm) - nearest = {} - smallest = 1000000000 - smallestBPM = 1000000000 - smallestTimbre = 1000000000 - smallestIntensity = 1000000000 - for vid in videos: - if vid.id != query.id: - comp_bpm = abs(querybpm - vid.audio_features['tempo']) - comp_timbre = abs(query.audio_features["timbre"] - vid.audio_features['timbre']) - comp_intensity = abs(query.audio_features["intensity"] - vid.audio_features['intensity']) - #comp = abs(query.audio_features["pitch"] - vid.audio_features['pitch']) - comp = abs(query.audio_features["frequency"] - vid.audio_features['frequency']) - - if searchforbpm: - if vid.id not in previous_list and comp < smallest and comp_bpm < smallestBPM:# and comp_timbre < smallestTimbre: - smallest = comp - smallestBPM = comp_bpm - smallestTimbre = comp_timbre - nearest = vid - else: - if vid.id not in previous_list and comp < smallest: - smallest = comp - smallestBPM = comp_bpm - smallestTimbre = comp_timbre - nearest = vid - #print("--- result",i['file'],i['average_frequency'],i['average_key'],"diff",comp) - # print(nearest) - previous_list.append(nearest.id) - - if len(previous_list) >= len(videos)-1: - previous_list.pop(0) - # print("getNearestPitch: previous_list, pop first") - # print("get_nearest",nearest.id) - return nearest - -def getNearest(k, array): - k = k / 10 # HACK - return min(enumerate(array), key=lambda x: abs(x[1]-k)) - - -################## MAIN ################## - -def main(): - print("---------------------------------------------------------------------------- ") - print("--------------------------------- POLYMATH --------------------------------- ") - print("---------------------------------------------------------------------------- ") - # Load DB - videos = read_library() - - for directory in ("processed", "library", "separated", "separated/htdemucs_6s"): - os.makedirs(directory, exist_ok=True) - - # Parse command line input - parser = argparse.ArgumentParser(description='polymath') - parser.add_argument('-a', '--add', help='youtube id', required=False) - parser.add_argument('-r', '--remove', help='youtube id', required=False) - parser.add_argument('-v', '--videos', help='video db length', required=False) - parser.add_argument('-t', '--tempo', help='quantize audio tempo in BPM', required=False, type=float) - parser.add_argument('-q', '--quantize', help='quantize: id or "all"', required=False) - parser.add_argument('-k', '--quantizekeepbpm', help='quantize to the BPM of the original audio file"', required=False, action="store_true", default=False) - parser.add_argument('-s', '--search', help='search for musically similar audio files, given a database id"', required=False) - parser.add_argument('-sa', '--searchamount', help='amount of results the search returns"', required=False, type=int) - parser.add_argument('-st', '--searchbpm', help='include BPM of audio files as similiarty search criteria"', required=False, action="store_true", default=False) - parser.add_argument('-m', '--midi', help='extract midi from audio files"', required=False, action="store_true", default=False) - - args = parser.parse_args() - - # List of videos to use - if args.videos is not None: - finalvids = [] - vids = args.videos.split(",") - print("process selected videos only:",vids) - for vid in vids: - v = [x for x in videos if x.id == vid][0] - finalvids.append(v) - videos = finalvids - - # List of videos to delete - if args.remove is not None: - print("remove video:",args.remove) - for vid in videos: - if vid.id == args.remove: - videos.remove(vid) - break - write_library(videos) - - # List of videos to download - newvids = [] - if args.add is not None: - print("add video:",args.add,"to videos:",len(videos)) - vids = args.add.split(",") - if "/" in args.add and not (args.add.endswith(".wav") or args.add.endswith(".mp3")): - print('add directory with wav or mp3 files') - videos = audio_directory_process(vids,videos) - elif ".wav" in args.add or ".mp3" in args.add: - print('add wav or mp3 file') - videos = audio_process(vids,videos) + if args.input: + input_tracks(nendo, args.input) + + if ( + args.process or + args.analyze or + args.stemify or + (args.quantize is not None) or + args.loopify + ): + # apply search + tracks = [] + if args.find is None: + tracks = nendo.filter_tracks(track_type="track") else: - videos = video_process(vids,videos) - newvids = vids - - # List of audio to quantize - vidargs = [] - if args.quantize is not None: - vidargs = args.quantize.split(",") - # print("Quantize:", vidargs) - if vidargs[0] == 'all' and len(newvids) != 0: - vidargs = newvids - - # MIDI - extractmidi = bool(args.midi) - if extractmidi: - global basic_pitch_model - basic_pitch_model = tf.saved_model.load(str(ICASSP_2022_MODEL_PATH)) - - # Tempo - tempo = int(args.tempo or 120) - - # Quanize: Keep bpm of original audio file - keepOriginalBpm = bool(args.quantizekeepbpm) - - # WIP: Quanize: Pitch shift before quanize - pitchShiftFirst = False - # if args.quantizepitchshift: - # pitchShiftFirst = True - - # Analyse to DB - print(f"------------------------------ Files in DB: {len(videos)} ------------------------------") - dump_db = False - # get/detect audio metadata - for vid in videos: - feature_file = f"library/{vid.id}.a" - # load features from disk - if os.path.isfile(feature_file): - with open(feature_file, "rb") as f: - audio_features = pickle.load(f) - # extract features + for search_value in args.find.split(","): + tracks = tracks + nendo.filter_tracks( + search_meta={"": search_value}, + track_type="track", + ) + + if args.process: + process_tracks( + nendo = nendo, + tracks = tracks, + analyze = True, + stemify = True, + quantize = True, + quantize_to_bpm = args.quantize or 120, + loopify = True, + n_loops = args.loop_num, + beats_per_loop = args.loop_beats, + ) else: - # Is audio file from disk - if len(vid.id) > 12: - print('is audio', vid.id, vid.name, vid.url) - file = vid.url - # if is mp3 file - if vid.url[-3:] == "mp3": - file = os.path.join(os.getcwd(), 'library', vid.id + '.wav') - # Is audio file extracted from downloaded video - else: - file = os.path.join(os.getcwd(), 'library', vid.id + '.wav') - - # Audio feature extraction - audio_features = get_audio_features(file=file,file_id=vid.id, extractMidi=extractmidi) - - # Save to disk - with open(feature_file, "wb") as f: - pickle.dump(audio_features, f) - - # assign features to video - vid.audio_features = audio_features - print( - vid.id, - "tempo", round(audio_features["tempo"], 2), - "duration", round(audio_features["duration"], 2), - "timbre", round(audio_features["timbre"], 2), - "pitch", round(audio_features["pitch"], 2), - "intensity", round(audio_features["intensity"], 2), - "segments", len(audio_features["segments_boundaries"]), - "frequency", round(audio_features["frequency"], 2), - "key", audio_features["key"], - "name", vid.name, + process_tracks( + nendo = nendo, + tracks = tracks, + analyze = args.analyze, + stemify = args.stemify, + quantize = args.quantize is not None, + quantize_to_bpm = args.quantize if args.quantize is not None else 120, + loopify = args.loopify, + n_loops = args.loop_num, + beats_per_loop = args.loop_beats, + ) + + if args.export and args.output_path is not None: + if args.find is not None: + search_values = args.find.split(",") + search_meta = { f"arg{i}": search_values[i] for i in range(len(search_values)) } + track_type = ["track"] + if args.find_stems is True: + track_type.append("stem") + if args.find_loops is True: + track_type.append("loop") + if args.find is None: + track_type.append("quantized") + filters = {} + if args.bpm_min is not None or args.bpm_max is not None: + track_type.append("quantized") + bpm_min = args.bpm_min or 0 + bpm_max = args.bpm_max or 999 + filters.update({"tempo": (float(bpm_min), float(bpm_max))}) + + found_tracks = nendo.filter_tracks( + filters=filters if len(filters) > 0 else None, + track_type=track_type, + search_meta=search_meta if args.find is not None else None, + ) + found_tracks = [ + ft for ft in found_tracks if ( + not ft.has_meta("stem_type") or + (args.find_stems is True and + (args.stem_type == "any" or + ft.get_meta("stem_type") == args.stem_type))) + ] + export_tracks( + nendo=nendo, + tracks=found_tracks, + output_folder=args.output_path, + export_format=args.output_format, ) - #dump_db = True - if dump_db: - write_library(videos) - - print("--------------------------------------------------------------------------") - - # Quantize audio - if args.search is None: - for vidarg in vidargs: - for idx, vid in enumerate(videos): - if vid.id == vidarg: - quantizeAudio(videos[idx], bpm=tempo, keepOriginalBpm = keepOriginalBpm, pitchShiftFirst = pitchShiftFirst, extractMidi = extractmidi) - break - if vidarg == 'all' and len(newvids) == 0: - quantizeAudio(videos[idx], bpm=tempo, keepOriginalBpm = keepOriginalBpm, pitchShiftFirst = pitchShiftFirst, extractMidi = extractmidi) - - # Search - searchamount = int(args.searchamount or 20) - searchforbpm = bool(args.searchbpm) - if args.search is not None: - for vid in videos: - if vid.id == args.search: - query = vid - print( - 'Audio files related to:', query.id, - "- Key:", query.audio_features['key'], - "- Tempo:", int(query.audio_features['tempo']), - ' - ', query.name, - ) - if args.quantize is not None: - quantizeAudio(query, bpm=tempo, keepOriginalBpm = keepOriginalBpm, pitchShiftFirst = pitchShiftFirst, extractMidi = extractmidi) - i = 0 - while i < searchamount: - nearest = get_nearest(query, videos, tempo, searchforbpm) - query = nearest - print( - "- Relate:", query.id, - "- Key:", query.audio_features['key'], - "- Tempo:", int(query.audio_features['tempo']), - ' - ', query.name, - ) - if args.quantize is not None: - quantizeAudio(query, bpm=tempo, keepOriginalBpm = keepOriginalBpm, pitchShiftFirst = pitchShiftFirst, extractMidi = extractmidi) - i += 1 - break if __name__ == "__main__": main() diff --git a/requirements.txt b/requirements.txt index c447f84..09430ed 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,8 @@ -crepe==0.0.13 -librosa==0.9.2 -numpy>=1.20 -pyrubberband==0.3.0 -sf_segmenter==0.0.2 -soundfile==0.11.0 -yt_dlp==2023.02.17 -demucs==4.0.0 -basic-pitch==0.2.0 -matplotlib -tensorflow==2.9; sys_platform == 'windows' or platform_machine != 'arm64' -tensorflow-macos; sys_platform == 'darwin' and platform_machine == 'arm64' -tensorflow-metal; sys_platform == 'darwin' and platform_machine == 'arm64' - +nendo==0.2.1 +nendo_plugin_classify_core==0.2.6 +nendo_plugin_quantize_core==0.2.6 +nendo_plugin_stemify_demucs==0.1.2 +nendo_plugin_loopify==0.1.6 +tensorflow +git+https://github.com/CPJKU/madmom.git@0551aa8 +yt_dlp>=2023.12.30 diff --git a/run_docker.sh b/run_docker.sh new file mode 100755 index 0000000..533e44f --- /dev/null +++ b/run_docker.sh @@ -0,0 +1,5 @@ +#!/bin/bash + + +mkdir -p processed separated library input +sudo docker run -v "$(pwd)"/processed:/polymath/processed -v "$(pwd)"/separated:/polymath/separated -v"$(pwd)"/library:/polymath/library -v "$(pwd)"/input:/polymath/input polymath python /polymath/polymath.py "$@" \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..192cd1d --- /dev/null +++ b/setup.py @@ -0,0 +1,16 @@ +from setuptools import setup + +setup( + name='polymath', + version='0.1.0', + packages=['.'], + install_requires=[ + "nendo>=0.1.2", + "nendo_plugin_classify_core>=0.2.3", + "nendo_plugin_quantize_core>=0.1.2", + "nendo_plugin_stemify_demucs>=0.1.0", + "nendo_plugin_loopify>=0.1.1", + "yt_dlp>=2023.11.16", + "tensorflow", + ], +)