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@@ -3,199 +3,161 @@ library_name: transformers
 tags: []
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-# Model Card for Model ID
+MusicLang : Controllable Symbolic Music Generation
+========================================================
 
-<!-- Provide a quick summary of what the model is/does. -->
+![MusicLang logo](https://github.com/MusicLang/musiclang/blob/main/documentation/images/MusicLang.png?raw=true "MusicLang")
 
 
+🎶  <b>&nbsp; You want to generate music that you can export to your favourite DAW in MIDI ?</b>
 
-## Model Details
 
-### Model Description
+🎛️ <b>&nbsp; You want to control the chord progression of the generated music ? </b>
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+🚀  <b>&nbsp; You need to run it fast on your laptop without a gpu ?</b>
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
+Here is MusicLang Predict, your controllable music copilot.
 
-<!-- Provide the basic links for the model. -->
+I just want to try !
+--------------------
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MA2mek826c05BjbWk2nRkVv2rW7kIU_S?usp=sharing)
 
-## Uses
+Go to our Colab, we have a lot of cool examples. From generating creative musical ideas to continuing a song with a specified chord progression.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+I am more serious about it
+--------------------------
 
-### Direct Use
+Install the musiclang-predict package :
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```bash
+pip install musiclang_predict
+```
 
-[More Information Needed]
+Then open your favourite notebook and start generating music in a few lines :
 
-### Downstream Use [optional]
+```python
+from musiclang_predict import MusicLangPredictor
+nb_tokens = 1024 
+temperature = 0.9  # Don't go over 1.0, at your own risks !
+top_p = 1.0 # <=1.0, Usually 1 best to get not too much repetitive music
+seed = 16  # change here to change result, or set to 0 to unset seed
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+ml = MusicLangPredictor('musiclang/musiclang-v2') # Only available model for now
 
-[More Information Needed]
+score = ml.predict(
+    nb_tokens=nb_tokens,  # 1024 tokens ~ 25s of music (depending of the number of instruments generated)
+    temperature=temperature,
+    topp=top_p,
+    rng_seed=seed # change here to change result, or set to 0 to unset seed
+)
+score.to_midi('test.mid') # Open that file in your favourite DAW, score editor or even in VLC
+```
 
-### Out-of-Scope Use
+You were talking about controlling the chord progression ?
+----------------------------------------------------------
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+You had a specific harmony in mind am I right ?
+That's why we allow a fine control over the chord progression of the generated music.
+Just specify it as a string like below, choose a time signature and let the magic happen.
 
-[More Information Needed]
+```python
+from musiclang_predict import MusicLangPredictor
 
-## Bias, Risks, and Limitations
+# Control the chord progression
+# Chord qualities available : M, m, 7, m7b5, sus2, sus4, m7, M7, dim, dim0.
+# You can also specify the bass if it belongs to the chord (eg : Bm/D)
+chord_progression = "Am CM Dm E7 Am" # 1 chord = 1 bar
+time_signature = (4, 4) # 4/4 time signature, don't be too crazy here 
+nb_tokens = 1024 
+temperature = 0.8
+top_p = 1.0
+seed = 42
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+ml = MusicLangPredictor('musiclang/musiclang-v2')
 
-[More Information Needed]
+score = ml.predict_chords(
+    chord_progression,
+    time_signature=time_signature,
+    temperature=temperature,
+    topp=top_p,
+    rng_seed=seed # set to 0 to unset seed
+)
+score.to_midi('test.mid', tempo=120, time_signature=(4, 4))
+```
 
-### Recommendations
+Disclaimer : The chord progression is not guaranteed to be exactly the same as the one you specified. It's a generative model after all.
+Usually it will happen when you use an exotic chord progression and if you set a high temperature.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+That's cool but I have my music to plug in ...
+------------------------------------------------
 
-## How to Get Started with the Model
+Don't worry, we got you covered. You can use your music as a template to generate new music.
+Let's continue some Bach music with a chord progression he could have used : 
+```python
+from musiclang_predict import MusicLangPredictor
+from musiclang_predict import corpus
 
-Use the code below to get started with the model.
+song_name = 'bach_847' # corpus.list_corpus() to get the list of available songs
+chord_progression = "Cm C7/E Fm F#dim G7 Cm"
+nb_tokens = 1024 
+temperature = 0.8 
+top_p = 1.0 
+seed = 3666 
 
-[More Information Needed]
+ml = MusicLangPredictor('musiclang/musiclang-v2')
 
-## Training Details
+score = ml.predict_chords(
+    chord_progression,
+    score=corpus.get_midi_path_from_corpus(song_name),
+    time_signature=(4, 4),
+    nb_tokens=1024,
+    prompt_chord_range=(0,4),
+    temperature=temperature,
+    topp=top_p,
+    rng_seed=seed # set to 0 to unset seed
+)
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+score.to_midi('test.mid', tempo=110, time_signature=(4, 4))
+```
+
+What's coming next ?
+---------------------
+
+We are working on a lot of cool features, some are already encoded in the model :
+- A control over the instruments used in each bar and their properties (note density, pitch range, average velocity)
+- Some performances improvements over the inference C script
+- A faster distilled model for real-time generation that can be embedded in plugins or mobile applications
+- An integration into a DAW as a plugin
+- Some specialized smaller models depending on our user's needs
+
+How does that work ? 
+---------------------
+
+If you want to learn more about how we are moving toward symbolic music generation, go to our [technical blog](https://musiclang.github.io/).
+The tokenization, the model are described in great details. 
+
+We are using a LLAMA2 architecture (many thanks to Andrej Karpathy awesome [llama2.c](https://github.com/karpathy/llama2.c)), trained on a large dataset of midi files (The CC0 licensed [LAKH](https://colinraffel.com/projects/lmd/)).
+We heavily rely on preprocessing the midi files to get an enriched tokenization that describe chords & scale for each bar.
+The is also helpful for normalizing melodies relative to the current chord/scale.
+
+
+Contributing & Contact us
+-------------------------
+
+We are looking for contributors to help us improve the model, the tokenization, the performances and the documentation.
+If you are interested in this project, open an issue, a pull request, or even [contact us directly](https://www.musiclang.io/contact).
+
+License
+-------
+
+Specific licenses applies to our models. If you would like to use the model in your product, please
+[contact us](https://www.musiclang.io/contact). We are looking forward to hearing from you !
+MusicLang Predict is licensed under the GPL-3.0 License.
+
+The MusicLang base language package on which the model rely ([musiclang package](https://github.com/musiclang/musiclang)) is licensed under the BSD 3-Clause License.