<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ModelScope on SoloSoft</title><link>https://www.solosoft.dev/tags/modelscope/</link><description>Recent content in ModelScope on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/modelscope/index.xml" rel="self" type="application/rss+xml"/><item><title>FunClip: Open-Source AI Audio Clipping and Processing</title><link>https://www.solosoft.dev/post/funclip-audio-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/funclip-audio-2026/</guid><description>&lt;p&gt;Audio editing typically requires manual waveform inspection and precise cutting to isolate the segments you need. FunClip, developed by the ModelScope team, changes this by applying AI-powered speech recognition and content understanding to automate audio clipping tasks.&lt;/p&gt;
&lt;p&gt;Built on top of ModelScope&amp;rsquo;s ecosystem of AI models, FunClip transcribes audio, identifies meaningful segments based on keyword or content criteria, and extracts them into separate files. This is invaluable for podcast producers, voiceover artists, transcription services, and anyone working with long audio recordings who needs to extract specific content.&lt;/p&gt;
&lt;h2 id="key-features"&gt;Key Features&lt;/h2&gt;
&lt;table&gt;
 &lt;thead&gt;
 &lt;tr&gt;
 &lt;th&gt;Feature&lt;/th&gt;
 &lt;th&gt;Description&lt;/th&gt;
 &lt;/tr&gt;
 &lt;/thead&gt;
 &lt;tbody&gt;
 &lt;tr&gt;
 &lt;td&gt;Automatic transcription&lt;/td&gt;
 &lt;td&gt;Converts speech to text with timestamps using ASR models&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;Keyword-based clipping&lt;/td&gt;
 &lt;td&gt;Extract segments containing specific words or phrases&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;Speaker diarization&lt;/td&gt;
 &lt;td&gt;Identify and separate clips by speaker&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;Batch processing&lt;/td&gt;
 &lt;td&gt;Process multiple audio files in a single run&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;Configurable output&lt;/td&gt;
 &lt;td&gt;Adjustable padding, format, and quality settings&lt;/td&gt;
 &lt;/tr&gt;
 &lt;/tbody&gt;
&lt;/table&gt;
&lt;h2 id="audio-processing-workflow"&gt;Audio Processing Workflow&lt;/h2&gt;

&lt;figure class="mermaid-wrapper not-prose" role="img" aria-label="Mermaid diagram"&gt;
 &lt;div class="mermaid-container"&gt;
 &lt;pre class="mermaid"&gt;flowchart LR
 A[Audio File] --&amp;gt; B[ASR Transcription&amp;lt;br/&amp;gt;ModelScope]
 B --&amp;gt; C[Timestamped Text]
 C --&amp;gt; D[Content Analysis]
 D --&amp;gt; E{Matches Criteria?}
 E --&amp;gt;|Yes| F[Extract Segment]
 E --&amp;gt;|No| G[Skip]
 F --&amp;gt; H[Merge &amp;amp; Export]
 H --&amp;gt; I[Clipped Audio Files]&lt;/pre&gt;
 &lt;script type="application/mermaid"&gt;flowchart LR
 A[Audio File] --&gt; B[ASR Transcription&lt;br/&gt;ModelScope]
 B --&gt; C[Timestamped Text]
 C --&gt; D[Content Analysis]
 D --&gt; E{Matches Criteria?}
 E --&gt;|Yes| F[Extract Segment]
 E --&gt;|No| G[Skip]
 F --&gt; H[Merge &amp; Export]
 H --&gt; I[Clipped Audio Files]&lt;/script&gt;
 &lt;/div&gt;
&lt;/figure&gt;&lt;p&gt;The workflow starts with automatic speech recognition that produces word-level timestamps. Content analysis then identifies segments matching user-defined criteria, extracts them with optional padding, and exports the results as individual audio files.&lt;/p&gt;</description></item></channel></rss>