Upload app.py

app.py

@@ -1,5 +1,3 @@
-import subprocess
-import os
 import gradio as gr
 import json
 from utils import *
@@ -8,64 +6,27 @@ from transformers import AutoTokenizer
 
 description = """
 <div>
-<a style="display:inline-block" href='https://github.com/suno-ai/bark'><img src='https://img.shields.io/github/stars/suno-ai/bark?style=social' /></a>
-<a style='display:inline-block' href='https://discord.gg/J2B2vsjKuE'><img src='https://dcbadge.vercel.app/api/server/J2B2vsjKuE?compact=true&style=flat' /></a>
-<a style="display:inline-block; margin-left: 1em" href="https://huggingface.co/spaces/suno/bark?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space%20to%20skip%20the%20queue-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a>
+<a style="display:inline-block" href='https://github.com/microsoft/muzic/tree/main/clamp'><img src='https://img.shields.io/github/stars/microsoft/muzic?style=social' /></a>
+<a style='display:inline-block' href='https://ai-muzic.github.io/clamp/'><img src='https://img.shields.io/badge/website-CLaMP-ff69b4.svg' /></a>
+<a style="display:inline-block" href="https://huggingface.co/datasets/sander-wood/wikimusictext"><img src="https://img.shields.io/badge/huggingface-dataset-ffcc66.svg"></a>
+<a style="display:inline-block" href="https://arxiv.org/abs/2106.01955"><img src="https://img.shields.io/badge/arXiv-2106.01955-b31b1b.svg"></a>
 </div>
-Bark is a universal text-to-audio model created by [Suno](www.suno.ai), with code publicly available [here](https://github.com/suno-ai/bark). \
-Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. \
-This demo should be used for research purposes only. Commercial use is strictly prohibited. \
-The model output is not censored and the authors do not endorse the opinions in the generated content. \
-Use at your own risk.
-"""
 
-article = """
-## 🌎 Foreign Language
-Bark supports various languages out-of-the-box and automatically determines language from input text. \
-When prompted with code-switched text, Bark will even attempt to employ the native accent for the respective languages in the same voice.
-Try the prompt:
-```
-Buenos días Miguel. Tu colega piensa que tu alemán es extremadamente malo. But I suppose your english isn't terrible.
-```
-## 🤭 Non-Speech Sounds
-Below is a list of some known non-speech sounds, but we are finding more every day. \
-Please let us know if you find patterns that work particularly well on Discord!
-* [laughter]
-* [laughs]
-* [sighs]
-* [music]
-* [gasps]
-* [clears throat]
-* — or ... for hesitations
-* ♪ for song lyrics
-* capitalization for emphasis of a word
-* MAN/WOMAN: for bias towards speaker
-Try the prompt:
-```
-" [clears throat] Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as... ♪ singing ♪."
-```
-## 🎶 Music
-Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. \
-Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics.
-Try the prompt:
-```
-♪ In the jungle, the mighty jungle, the lion barks tonight ♪
-```
-## 🧬 Voice Cloning
-Bark has the capability to fully clone voices - including tone, pitch, emotion and prosody. \
-The model also attempts to preserve music, ambient noise, etc. from input audio. \
-However, to mitigate misuse of this technology, we limit the audio history prompts to a limited set of Suno-provided, fully synthetic options to choose from.
-## 👥 Speaker Prompts
-You can provide certain speaker prompts such as NARRATOR, MAN, WOMAN, etc. \
-Please note that these are not always respected, especially if a conflicting audio history prompt is given.
-Try the prompt:
-```
-WOMAN: I would like an oatmilk latte please.
-MAN: Wow, that's expensive!
-```
-## Details
-Bark model by [Suno](https://suno.ai/), including official [code](https://github.com/suno-ai/bark) and model weights. \
-Gradio demo supported by 🤗 Hugging Face. Bark is licensed under a non-commercial license: CC-BY 4.0 NC, see details on [GitHub](https://github.com/suno-ai/bark).
+## ℹ️ How to use this demo?
+1. Enter a query in the text box.
+2. Click "Submit" and wait for the result.
+3. It will return the most matching music score from the WikiMusictext dataset (1010 scores in total).
+
+## ❕Notice
+- The text box is case-sensitive.
+- You can enter longer text for the text box, but the demo will only use the first 128 tokens.
+- The returned results include the title, artist, genre, description, and the score in ABC notation.
+- The genre and description may not be accurate, as they are collected from the web.
+- The demo is based on CLaMP-S/512, a CLaMP model with 6-layer Transformer text/music encoders and a sequence length of 512.
+
+## 🔠👉🎵 Semantic Music Search
+Semantic search is a technique for retrieving music by open-domain queries, which differs from traditional keyword-based searches that depend on exact matches or meta-information. This involves two steps: 1) extracting music features from all scores in the library, and 2) transforming the query into a text feature. By calculating the similarities between the text feature and the music features, it can efficiently locate the score that best matches the user's query in the library.
+
 """
 examples = [
     "Jazz standard in Minor key with a swing feel.",
@@ -190,7 +151,7 @@ def semantic_music_search(query):
         key_cache = torch.load(f)
         
     # encode query
-    query_ids = encoding_data([query], QUERY_MODAL)
+    query_ids = encoding_data([unidecode(query)], QUERY_MODAL)
     query_feature = get_features(query_ids, QUERY_MODAL)
 
     key_filenames = key_cache["filenames"]
@@ -225,5 +186,4 @@ gr.Interface(
     outputs=[output_title, output_artist, output_genre, output_description, output_abc],
     title="🗜️ CLaMP: Semantic Music Search",
     description=description,
-    article=article,
     examples=examples).launch()