Speech Recognition on SoloSoft

AutoCut: AI-Powered Automatic Video Editing

Mon, 01 Jan 0001 00:00:00 +0000

Video editing is one of the most time-consuming creative tasks, especially the tedious process of cutting out silences, stumbles, and filler words from talking-head videos. AutoCut, created by mli, solves this problem with an AI-powered pipeline that automatically analyzes audio tracks and removes everything a human editor would cut.

The tool processes video files through speech recognition, identifies segments with meaningful speech, and produces a clean edit that maintains natural pacing. The result is a polished video without hours of manual timeline work.

Core Features

Feature	Description
Silence removal	Automatically detects and removes pauses longer than a configurable threshold
Filler word detection	Identifies “um”, “uh”, “like”, and other verbal fillers for removal
Speech recognition	Uses Whisper or other ASR engines for accurate transcription
Configurable thresholds	Adjust aggressiveness of silence and filler removal
Batch processing	Process multiple videos in a single run

Editing Pipeline

flowchart LR
 A[Raw Video] --> B[Audio Extraction]
 B --> C[Speech Recognition<br/>Whisper]
 C --> D[Segment Analysis]
 D --> E{Silence or Filler?}
 E -->|Yes| F[Mark for Removal]
 E -->|No| G[Keep Segment]
 F --> H[Timeline Assembly]
 G --> H
 H --> I[Export Edited Video]

The pipeline begins with audio extraction from the source video. Whisper transcribes the speech, and each segment is analyzed for silence duration and filler word presence. Marked segments are removed, and the remaining clips are assembled into a seamless final video.

Faster-Whisper: 4x Faster Speech Recognition with CTranslate2

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI’s Whisper model was a breakthrough in automatic speech recognition (ASR), demonstrating that large-scale weakly supervised training could produce a model with robust multilingual transcription capabilities. However, the standard PyTorch implementation left significant performance on the table. Faster-Whisper, developed by SYSTRAN, addresses this gap through a CTranslate2-based reimplementation that achieves dramatic speed improvements.

CTranslate2 is an inference engine specifically optimized for Transformer models, supporting INT8 and FP16 quantization, CPU-optimized matrix operations, and efficient beam search decoding. By reimplementing Whisper’s architecture on this engine, Faster-Whisper achieves 3-4x speed improvements while reducing memory consumption by approximately half.

For organizations running speech transcription at scale, these efficiency gains translate directly into cost savings. A transcription pipeline that processes thousands of hours of audio per day can reduce GPU hours by 60-75% simply by switching from Whisper to Faster-Whisper, with no loss in transcription quality.

FunClip: Open-Source AI Audio Clipping and Processing

Mon, 01 Jan 0001 00:00:00 +0000

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.

Built on top of ModelScope’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.

Key Features

Feature	Description
Automatic transcription	Converts speech to text with timestamps using ASR models
Keyword-based clipping	Extract segments containing specific words or phrases
Speaker diarization	Identify and separate clips by speaker
Batch processing	Process multiple audio files in a single run
Configurable output	Adjustable padding, format, and quality settings

Audio Processing Workflow

flowchart LR
 A[Audio File] --> B[ASR Transcription<br/>ModelScope]
 B --> C[Timestamped Text]
 C --> D[Content Analysis]
 D --> E{Matches Criteria?}
 E -->|Yes| F[Extract Segment]
 E -->|No| G[Skip]
 F --> H[Merge & Export]
 H --> I[Clipped Audio Files]

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.

MLX-Audio: TTS, STT, and STS Library Optimized for Apple Silicon

Mon, 01 Jan 0001 00:00:00 +0000

Apple Silicon Macs equipped with M-series chips – from the M1 through the latest M4 Ultra – pack extraordinary computational power, particularly for machine learning workloads. Their unified memory architecture allows models to access large amounts of fast memory without the bottlenecks of traditional CPU-GPU data transfer. MLX-Audio, an open-source Python library built on Apple’s MLX framework, is purpose-built to exploit this hardware advantage for all things audio AI.

MLX-Audio provides a unified interface for text-to-speech, speech-to-text, and speech-to-speech conversion, supporting dozens of models from OpenAI’s Whisper (for transcription) to Kokoro and VoiceCraft (for synthesis). It brings together capabilities that are typically scattered across multiple libraries and frameworks, all optimized to run efficiently on Mac hardware.