DEV Community: Abel Harriman

From Raw Audio to Searchable Text: An Engineering Guide to Audio Denoising, Vocal Separation, and ASR

Abel Harriman — Fri, 23 Oct 2026 07:23:28 +0000

This post walks the full audio-AI pipeline —

denoising → vocal/instrumental separation → voice activity detection → automatic speech recognition (ASR) → speaker diarization → subtitles/summaries/translation

— from an engineering point of view. Using DeVoice as a working example, you'll get a production-minded workflow plus runnable Python and ffmpeg code for meeting notes, short-form captions, podcast repurposing, and call-center QA.

1. Why audio has to become "structured"

In most systems, audio and video are unstructured data. A one-hour meeting recording, a 40-minute podcast, or a product walkthrough video is, to a program, just a stream of samples. You can't run a SELECT on it, you can't search for a keyword, you can't locate "who said what at which minute," and you can't feed it directly into retrieval, analytics, or an LLM Q&A pipeline.

Turning audio into structured data is a kind of modality reduction:

waveform/video  →  clean vocals  →  timestamped text  →  speaker-labeled document  →  summary/translation/knowledge graph

Once that conversion exists, you can:

Search hundreds of hours of recordings and jump to the exact moment a decision was made;
Repurpose one video into captions, a blog post, podcast show notes, and social clips;
Analyze support calls for compliance, review sales talk tracks, and extract action items from meetings;
Improve accessibility and reach with automatic captions and cross-language translation for global audiences.

The full pipeline looks like this:

flowchart LR
    A[Raw audio/video<br/>MP3/WAV/MP4/M4A...] --> B[Preprocessing<br/>resample/loudness/split]
    B --> C[AI denoising<br/>speech vs non-speech]
    C --> D[Vocal/instrumental<br/>separation]
    D --> E[VAD voice activity]
    E --> F[ASR acoustic model<br/>+ word timestamps]
    F --> G[Speaker diarization]
    G --> H[Punctuation / ITN / language ID]
    H --> I[Structured output<br/>SRT/VTT/TXT/DOCX/CSV]
    I --> J[Post-processing<br/>translation/summary/AI chat/mind map]

Let's go through it stage by stage.

2. Denoising first: why "clean before you recognize" is non-negotiable

2.1 Noise is the #1 killer of ASR accuracy

Speech recognition quality is typically measured with WER (Word Error Rate). A model that performs well on clean audio degrades sharply when it meets keyboard clicks, air-conditioner hum, room reverb, outdoor wind, or background chatter. The reason is simple: the model has to distinguish phonemes in a spectrogram where speech and non-speech overlap. Noise masks consonants, shifts formants, and produces plenty of "hallucinated" words.

That's why the standard engineering practice is speech enhancement before recognition.

2.2 Classic methods: the limits of spectral subtraction and Wiener filtering

Early denoising worked in the frequency domain by "subtraction." Take spectral subtraction:

Grab a snippet of pure noise and estimate its average power spectrum |N(ω)|²;
Run STFT on the noisy signal to get |Y(ω)|²;
Estimate clean speech with |Ŝ(ω)|² = |Y(ω)|² − α|N(ω)|², then reconstruct using the original phase.

The drawbacks are obvious:

It assumes stationary noise. Air-conditioner hum is fine, but non-stationary noise — keyboards, traffic, passers-by — can't be represented by a single noise sample;
Subtract too much and you damage the voice, producing a muffled, watery "flanging" artifact;
Subtract too little and you get "musical noise" — isolated frame-level spectral blobs that sound like electronic chirping.

Wiener filters and statistical-model methods are smoother but still rely on estimating a fixed noise floor and subtracting it, which fails whenever speech and noise overlap heavily.

2.3 Deep learning: from "subtracting" to "separating"

The modern mindset shifts the problem entirely — instead of estimating noise, the model learns what speech is and explicitly splits the signal into two streams:

a voice stream;
an everything-else stream (noise, reverb, background music).

Two dominant technical routes:

1) Time-frequency masking. On the STFT spectrogram, the model predicts a mask (e.g., an Ideal Ratio Mask, IRM) that decides, per time-frequency bin, how much of the energy belongs to speech, then applies the mask to recover clean vocals. U-Net-style encoder-decoder networks with skip connections are a common architecture.

2) Waveform-domain separation. Waveform-domain U-Nets such as Demucs separate signals end-to-end directly on the waveform, sidestepping the hard phase-estimation problem; they work particularly well for music source separation.

The key advantage is that these methods act on speech characteristics rather than a fixed noise fingerprint, so they handle non-stationary noise (wind, reverb, crowds, keyboards, traffic) while preserving the naturalness of the voice.

2.4 Vocal isolation: a "superset" of denoising

When you're dealing with songs, videos with background music, or live recordings, the need upgrades from denoising to source separation: split the vocals (acapella) from the instrumental. It shares the same underlying target-source-separation engine, just with finer-grained targets.

The instrumental can be used for karaoke, remixes, and sampling; the clean vocals feed covers, dubbing, re-mixing, or transcription.

2.5 Doing this step with DeVoice

The DeVoice Background Noise Remover combines three things into one in-browser workbench (it runs in the browser, requires no install, and works on Windows/macOS/iOS/Android):

AI background denoising — removes hiss, hum, wind, echo/reverb, crowd noise, keyboard clicks, traffic, and background music;
Vocal/instrumental separation — outputs acapella and instrumental stems for karaoke, remixes, and sampling;
One-click transcription — send the clean vocals straight to ASR without switching tools.

It accepts MP3 / WAV / M4A / FLAC / MP4; the guest free tier handles clips up to 5 minutes. The workflow is three steps: upload → AI processing → A/B preview and download.

Engineering note: placing denoising before ASR is a near-zero-cost optimization that meaningfully lowers downstream WER. The payoff is largest for high-noise recordings — phone footage shot outdoors, home recordings, and omnidirectional conference-room mics.

3. ASR internals: what happens between waveform and text

After denoising, we enter automatic speech recognition. A modern ASR system typically contains these modules.

3.1 Voice Activity Detection (VAD)

VAD decides which segments contain speech, cutting out silence and long pauses. Common implementations include the lightweight WebRTC VAD and the neural Silero VAD. It helps by:

reducing wasted computation (silence never reaches the engine);
lowering the memory pressure of long audio;
providing boundaries for chunking and speaker segmentation.

3.2 Feature extraction

The raw waveform (e.g., 16 kHz, 16-bit) isn't fed directly to the network. It's first converted into acoustic features:

framing (e.g., 25 ms frames, 10 ms hop) with a window function;
FFT/STFT to obtain the spectrogram;
a Mel filter bank and logarithm, yielding log-mel filterbank (fbank) features, often 80 dimensions.

The Mel scale mimics the human ear's greater sensitivity to low frequencies and captures the spectral envelope compactly.

3.3 Acoustic model and decoding

The mainstream end-to-end families include:

CTC, which introduces a blank label to solve frame-to-label alignment — fast to train and convenient for forced alignment;
RNN-Transducer (RNN-T), which naturally supports streaming (encoder + prediction network + joint network) and powers many online real-time transcription services;
Attention encoder-decoder / Conformer / Whisper-style models. Conformer combines CNN local modeling with Transformer global attention and excels on long, multilingual audio; large multilingual models such as Whisper fold recognition, language identification, and translation into a single framework.

Post-decoding steps include:

punctuation restoration (raw output often has none);
ITN (inverse text normalization), converting spoken forms into written numbers, dates, and currency;
capitalization and sentence segmentation.

3.4 Word-level timestamps

Text alone isn't enough for captions and navigation — you need a timeline. Timestamps usually come from CTC/attention forced alignment, mapping each token back to a frame and then to milliseconds. DeVoice provides word-level millisecond timestamps, meaning each word gets precise start/end boundaries rather than a rough sentence-level range. That's essential for generating SRT/VTT, building keyword jump-to-time, and rendering highlighted subtitles.

3.5 Multilingual identification

Global products handle 100+ languages. In practice, an LID (Language Identification) step first detects the language, then routes the audio to the right multilingual model branch. DeVoice auto-detects 100+ languages and can translate after recognition, which is especially useful for YouTube/TikTok content aimed at overseas audiences and for cross-border meetings.

4. Speaker diarization: answering "who said this"

Meetings, interviews, and support calls have multiple speakers. Diarization determines who speaks when, following a typical pipeline:

Segmentation — cut the audio into short, single-speaker segments;
Speaker embeddings — encode each segment into a vector that represents a speaker's voice, such as an x-vector;
Clustering — group similar vectors into clusters, each representing one speaker (Speaker 1, Speaker 2, …);
Alignment with ASR — tag each recognized utterance with its speaker.

DeVoice transcription offers optional speaker diarization; when enabled, the exported transcript carries Speaker 1 / Speaker 2 labels — a must-have for meeting notes, interviews, and call-center QA.

5. Post-processing: from raw transcript to a usable asset

The verbatim transcript is only the starting point. The real time savings come afterward:

Subtitles — SRT and VTT carry timelines and can be muxed or attached directly to video;
Multi-format export — TXT for reading, DOCX for editing, CSV with timestamps/speakers for programmatic analysis;
AI summaries — meeting minutes, key points, and action items by template;
Translation — multilingual subtitles and transcripts;
AI chat — ask questions over the whole transcript ("What was the customer's core complaint?");
Mind maps — structure long content into a tree outline for review.

On the results page, DeVoice lets you translate, summarize, chat with, and build a mind map from the transcript, then export SRT / VTT / TXT / DOCX / CSV as needed.

6. A hands-on DeVoice workflow

6.1 Denoising / vocal separation (3 steps)

Open the Background Noise Remover and upload a local file or video (MP3/WAV/M4A/FLAC/MP4);
Wait for AI processing — it denoises automatically and can split vocals from the instrumental on demand;
Use the before/after preview to confirm the voice stays natural (no watery artifacts), download the clean audio, or continue straight to transcription.

6.2 Audio-to-text (with batching)

Open Audio to Text and upload a file or paste a video/audio URL;
Process up to 5 tasks in parallel when batch uploading;
Configure options: speaker diarization on/off, AI summary type, and language (auto-detect available);
After recognition, translate, summarize, chat with, or build a mind map from the transcript;
Export SRT / VTT / TXT / DOCX / CSV for your use case.

Everything runs in the browser, so there's no heavy local software to install and no GPU environment to configure.

7. Developer use cases with runnable code

Note: DeVoice is an in-browser online tool that runs up to 5 tasks in parallel. The code below targets the two steps developers can actually automate: ① pre-upload preprocessing with ffmpeg, and ② batch post-processing of the exported SRT/CSV artifacts. Everything is Python 3 and ready to run.

7.1 Use case 1: automatic captions for short video / courses

Pain point: manually transcribing and timing captions takes several times the length of the video, and timelines are error-prone.

Solution: video → DeVoice transcription (with word timestamps) → export SRT/VTT → attach to the video or player.

(1) ffmpeg preprocessing into an ASR-friendly format

# Extract the audio track and convert to 16kHz mono 16-bit PCM (a common ASR input)
ffmpeg -i input.mp4 -vn -ac 1 -ar 16000 -c:a pcm_s16le output.wav

# Loudness normalization (EBU R128) to reduce recognition variance from level differences
ffmpeg -i input.wav -af loudnorm=I=-16:LRA=11:TP=-1.5 output_norm.wav

# Split long audio into 10-minute chunks for parallel upload (-c copy avoids re-encoding)
ffmpeg -i output_norm.wav -f segment -segment_time 600 -c copy chunk_%03d.wav

(2) Parse SRT and convert to VTT / plain text in Python

import re, csv
from pathlib import Path

def parse_srt(path):
    """Parse SRT -> [(index, start_ms, end_ms, text), ...]"""
    raw = Path(path).read_text(encoding="utf-8-sig")
    blocks = re.split(r"\n\s*\n", raw.strip())
    items = []
    for b in blocks:
        lines = [l for l in b.strip().splitlines() if l.strip()]
        if len(lines) < 3:
            continue
        idx = lines[0].strip()
        m = re.match(r"(\d+):(\d+):(\d+)[,.](\d+)\s*-->\s*(\d+):(\d+):(\d+)[,.](\d+)", lines[1])
        if not m:
            continue
        g = list(map(int, m.groups()))
        start = ((g[0]*60 + g[1])*60 + g[2])*1000 + g[3]
        end   = ((g[4]*60 + g[5])*60 + g[6])*1000 + g[7]
        items.append((idx, start, end, " ".join(lines[2:])))
    return items

def ms_to_vtt(ms):
    h, ms = divmod(ms, 3600000)
    m, ms = divmod(ms, 60000)
    s, ms = divmod(ms, 1000)
    return f"{h:02d}:{m:02d}:{s:02d}.{ms:03d}"

def srt_to_vtt(items):
    out = ["WEBVTT", ""]
    for _, s, e, text in items:
        out.append(f"{ms_to_vtt(s)} --> {ms_to_vtt(e)}")
        out.append(text)
        out.append("")
    return "\n".join(out)

def find_keyword(items, keyword):
    """Locate a keyword: return every timestamp (seconds) where it appears."""
    hits = []
    for _, s, e, text in items:
        if keyword.lower() in text.lower():
            hits.append((round(s/1000, 2), text))
    return hits

if __name__ == "__main__":
    items = parse_srt("demo.srt")
    Path("demo.vtt").write_text(srt_to_vtt(items), encoding="utf-8")
    Path("demo.txt").write_text("\n".join(t for *_, t in items), encoding="utf-8")
    print("subtitle cues:", len(items))
    print("'pricing' appears at:", find_keyword(items, "pricing"))

With millisecond timestamps you can also make clickable captions that seek the video, auto-cut clips around keywords, or burn in highlighted subtitles.

7.2 Use case 2: meeting / interview notes

Pain point: a one-hour meeting takes 30+ minutes to turn into notes, and you can't tell who owned which action item.

Solution: enable speaker diarization → export the speaker-labeled transcript → use AI summaries for decisions and action items → export DOCX to circulate.

After exporting CSV, you can aggregate speaking time per speaker to understand the meeting's structure:

import csv, re
from collections import Counter

def load_transcript_csv(path):
    """Read a transcript CSV with start/end/speaker/text columns."""
    rows = []
    with open(path, encoding="utf-8-sig", newline="") as f:
        for r in csv.DictReader(f):
            rows.append(r)
    return rows

def speaker_stats(rows):
    speak_time, speak_words = Counter(), Counter()
    for r in rows:
        spk = r.get("speaker") or r.get("Speaker") or "Unknown"
        try:
            dur = float(r.get("end", 0)) - float(r.get("start", 0))
        except ValueError:
            dur = 0
        speak_time[spk] += dur / 1000.0          # ms -> seconds
        speak_words[spk] += len(re.findall(r"\w+", r.get("text", "")))
    return speak_time, speak_words

if __name__ == "__main__":
    rows = load_transcript_csv("meeting.csv")
    st, sw = speaker_stats(rows)
    for spk in st:
        print(f"{spk}: spoke {st[spk]:.0f}s, {sw[spk]} words")

Combined with DeVoice's AI summaries, you get a structured "decisions / action items / owners / deadlines" note without re-watching the recording.

7.3 Use case 3: call-center / sales QA

Pain point: QA teams only sample-listen to calls, so coverage is low, subjective, and delayed.

Solution: batch transcription → diarization to separate agent and customer → keyword and talk-track detection → warehouse for full-population QA.

The web app runs up to 5 tasks in parallel, and locally you can batch-process the exported files with a thread pool using the same concurrency:

import json
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

# Align with the web app's "up to 5 tasks in parallel" to avoid over-subscription
MAX_WORKERS = 5

def analyze_one(srt_path):
    """QA for a single call: check compliance phrases and risk terms."""
    items = parse_srt(srt_path)          # reuse the parser from 7.1
    full = " ".join(t for *_, t in items)
    rules = {
        "recording_disclosure": ["this call is recorded", "calls are recorded"],
        "risk_terms": ["refund", "complaint", "lawyer", "cancel"],
    }
    result = {"file": srt_path.name, "duration_sec": round(items[-1][2]/1000, 1)}
    for k, kws in rules.items():
        result[k] = [w for w in kws if w.lower() in full.lower()]
    return result

def batch(folder):
    files = sorted(Path(folder).glob("*.srt"))
    with ThreadPoolExecutor(max_workers=MAX_WORKERS) as ex:
        return list(ex.map(analyze_one, files))

if __name__ == "__main__":
    report = batch("calls")
    Path("qa_report.json").write_text(
        json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
    print(f"QA complete for {len(report)} calls")

7.4 Use case 4: repurposing podcasts / global content

From a single English podcast or YouTube video, DeVoice can run denoising → transcription → translation → summary in one pass, then export:

SRT/VTT: multilingual captions that improve accessibility and watch time;
TXT/DOCX: rewritten into a blog post or article;
Mind map: chapter structure for show notes and chapter markers.

One source asset multiplies into captions, articles, short-video scripts, and social posts — the standard "produce once, distribute everywhere" approach.

8. Engineering pitfalls and optimization tips

Denoising isn't "more is better." Over-processing creates watery artifacts and swallows breath sounds and weak syllables, hurting naturalness and ASR. Always A/B-test by ear and keep natural breathing.
Leave headroom while recording. Clipping distortion cannot be fixed in post; keep peaks around -6 dB.
Normalize the sample rate. 16 kHz mono is a common ASR input — convert with ffmpeg before upload to avoid resampling issues.
Split long audio before parallelizing. Chunk at VAD boundaries or fixed durations to exploit the 5-task parallelism and to re-upload only one segment if something fails.
Apply a tolerance when merging chunk timestamps. Add each chunk's global start offset when concatenating subtitles, or the timeline will drift.
Break captions along semantic boundaries. Word timestamps give points; readable captions need re-wrapping by punctuation and line length so no line is too long.
Confirm the language for mixed speech. Code-switched audio with jargon benefits from auto-detection plus a manual language check.
Expect some diarization error. Overlapping speech, fast turns, and far-field voices cause segmentation mistakes; correct speaker labels manually for high-stakes transcripts.

9. Copyright and compliance

When separating vocals, removing vocals, or downloading online audio/video, make sure you own the content or have authorization; stay within fair use, licensing, and permitted derivative use.
When transcribing and QA-ing support calls or meetings, honor local recording-consent laws (e.g., "this call is being recorded") and handle personal data with storage controls and redaction.
Review the data-handling and privacy policy of any audio tool; evaluate sensitive recordings before sending them to a cloud service.

10. Wrapping up

The real engineering value of audio AI isn't the single "speech-to-text" step — it's connecting the whole pipeline. Deep-learning source separation cleanly strips vocals from noise and background music; modern ASR produces multilingual text with word-level timestamps; diarization recovers who spoke when; and captions, summaries, translation, and chat turn unstructured audio/video into searchable, analyzable, redistributable data assets.

For individual developers and small teams, you don't have to train models or deploy GPU services yourself. An in-browser tool like DeVoice bundles denoising, vocal separation, transcription, diarization, multilingual support, summarization, and multi-format export. As a developer, you only own the two ends — preprocessing and post-processing of the artifacts — to cheaply build meeting-notes, auto-captioning, and call-QA applications.

If you're building anything around audio/video, start by turning your next recording into a structured document with timestamps and speaker labels, and run the pipeline yourself.

Links:

AI audio to text: https://devoice.io/audio-to-text
Online background noise remover / vocal isolation: https://devoice.io/remove-background-noise

How to Block Twitch Ads in 2022？

Abel Harriman — Tue, 17 May 2022 09:17:18 +0000

Twitch is one of the most well-known streaming platforms in the world, where users can watch live and recorded videos from their favorite streamers and give them their own virtual gifts. In the process, users will encounter various advertisements, which is the way for streaming media platforms to earn advertising fees. As users, we have no option to block ads, but are forced to watch these ads that may appear at any time.

What are the types of ads on Twitch?

There are many types of Twitch ads. As of 2022, the Twitch platform will present the following four types of ads:

Pre-roll ads:
Pre-roll ads are video ads that viewers play when they enter a new Twitch live channel. During the playback of the advertisement, the user cannot see the live broadcast screen nor hear the live broadcast sound. This type of advertisement is triggered by the Twitch platform.
Insert ad:
Insert ads are video ads inserted by viewers while watching a live broadcast. When the advertisement is played, the main screen plays the inserted advertisement content, and the audio plays the advertisement content. When the inserted advertisement is played in the form of picture-in-picture, the live screen moves to the upper right corner in the form of a small window, above the chat content, and there is no audio output on the screen. The ad is triggered by the host playing.
Streaming Display Ads:
Streaming display ads are 10-second display ads that appear on the channel. Viewers can see and hear the anchor's voice when these ads are displayed. Creators don't have to do anything different to use it. When the advertisement is playing, the live broadcast screen is reduced, and the streaming display advertisement appears below or on the side; when the advertisement is played, the live broadcast screen is restored.
Audio ad:
Audio ads are designed to allow viewers to watch the stream at 100% of its original size while the ad is playing. Like all ad breaks, audio ads are initiated by the host, who receives a share of the ad playback.
From a Twitch user's perspective, being forced to watch an ad while watching a video is a terrible experience, so ad blockers are all the more important.

How to Block Twitch Ads in 2022？

There are three simple, effective ways to block the above Twitch ad types:

Install the free Twitch ad blocking extension
Use an ad-blocking browser
Download the Adblock app

Method 1: Using free Twitch ad blocking extension

Adblock for Twitch™ is a browser extension that filters out dynamic ads in live or recorded video by temporarily reducing the quality of Twitch video.

How to use Adblocker for Twitch™?

Step 1: Install the free Twitch ad blocking extension Adblocker for Twitch™;
Step 2: Open Twitch and start Adblocker for Twitch™;
Step 3: When the extension is running, you can view the number of filtered ads in this window.

Pro:

Free to use forever；
High advertising filtering efficiency and easy to use;
Open source.

Con:

Static ad filtering is not supported temporarily;
For computer use only

Method 2: Use an ad blocking browser

In addition to browser extensions that specifically filter Twitch ads, you can also use an ad-blocking browser to block Twitch ads on your pages, such as the Brave web browser that blocks all third-party ads and tracking by default, which means that when you use it, ads will be automatically filtered out.

How do I use the Brave web browser?

Enter the Brave web browser installation interface;
Install it on your computer,
Run it and it will automatically turn on ad blocking.

Pro

Does not affect the playback speed of Twitch videos;
Free installation;
Open source.

Con

Pages take longer to load;
May cause Twitch streams to buffer;
Can't block pre-roll or mid-roll.

Method 3: Download the Twitch Adblock app

Of course, Twitch also serves as an app for users to watch live streams and videos on mobile, and downloading a Twitch Adblock app is the best option.
The best Adblock app for android and ios phones is AdGuard.

How to use the AdGuard app for android ?

Download AdGuard for Android;
Install the app (follow the instructions on the download page)
Run the application and install the HTTPS certificate

How to use the AdGuard app for ios?

- Download AdGuard for iOS from the App Store;
- Go to your iOS device's settings and enable the Safari content blocker setting for AdGuard;
- Watch twitch.tv ad-free in Safari.

Pro

Mobile terminal available;
Easy to install and use.
Protect users' privacy information

Con

The free version does not filter ads within the Twitch app.

Conclusion

An ad blocker is an important tool to improve the user experience on the online platform. Every user should choose a reliable ad blocker to prevent their private information from being leaked and the computer or network crashing.
The above are three reliable and effective operation guides for filtering Twitch ads, I hope they will help you.

7 Best Video Downloaders in 2022

Abel Harriman — Thu, 24 Mar 2022 10:22:57 +0000

With the rapid development of streaming media platforms in recent years, more and more viewers are not satisfied with watching videos online, because watching videos is the best choice to kill time in an environment without an occasional internet connection.
What are the criteria for the best video downloader?

Safe and reliable;
ensure video quality;
High flexibility of use;
Simple operation and time saving

The following are the best video downloaders I have used in 2022.

Best Video Downloader on PC in 2022

4K Video Downloader

4K Video Downloader, as one of the most well-known video downloaders, has this powerful download function. It is a video download software for macOS, Windows and Linux, and Android.

After successful installation, users can copy and paste the video links of most video platforms in the 4K Video Downloader application to complete the download. The downloaded videos are saved in 4K Video Downloader, which is easy for users to find.

Main advantages:

Flexible format conversion: 4K Video Downloader supports downloading of dozens of streaming media formats, mainly video, audio, etc.;
High-quality downloads: it offers downloads in the highest resolution;
Batch download: Users can choose to download videos in batches to save more waiting time;
Multi-system compatibility: suitable for users of macOS, Windows and Linux, and Android systems;
Compatible with many video sites: Save video and audio from YouTube, Vimeo, TikTok, SoundCloud, Bilibili, Niconico, Flickr, Facebook, Instagram, DailyMotion, Naver TV, Likee and Tumblr. Download streams from Twitch and YouTube Gaming.

Main shortcoming：

Users of the free plan have far fewer features than paid plans;
Downloader may have parsing and lag issues

Website: https://www.4kdownload.com/

Internet Download Manager (IDM)

Internet Download Manager (IDM) is a video download manager that increases download speed. It is the most famous and reliable video downloader besides 4K Video Downloader.

IDM provides users with a manageable download list, optimizes the user experience, and is widely praised.

Main advantages:

Faster and more reliable downloads: IDM truly increases the speed of video downloads, and will quickly recover interrupted downloads caused by problems such as disconnection and power outages;
Dynamic file segmentation technology: Unlike other download managers and accelerators, IDM dynamically segments downloaded files during the download process and reuses available connections without additional connection and login stages for optimal acceleration performance.
Wide variety of browser integrations: IDM supports all popular browsers and it integrates seamlessly into Google Chrome, FireFox, Microsoft Edge, Opera, Safari, Internet Explorer, AOL, MSN, Maxthon and all other popular browsers.
Free Trial: IDM has a 30-day free trial plan.

Main shortcoming:

There may be a problem with browser integration.

Website: https://www.internetdownloadmanager.com/

Vimeomate

Vimeomate is best known as a Google Chrome extension, also known as "Simple Vimeo Downloader". It has been online for three years and is the exclusive Vimeo downloader with the highest praise rate and download volume.

At present, Vimeomate has launched a new online version and a Microsoft version, which is one of the safest and most reliable free Vimeo downloaders.

Main advantages:

Simple operation: it only takes 1 minute to easily download a Vimeo video;
Various usage environments: Whether it is a Vimeo private video without a download button, a paid rental Vimeo video with a viewing period, or a Vimeo video embedded in a personal website, all support downloading.
Free forever: Vimeomate has no paid plans, all features are free to use
HD quality: Vimeomate lets users get multiple video qualities such as 240p, 360p, 540p, 720p, 1080p and even 4K.

Main shortcoming:

Unable to view download history: As a registration-free online site and extension, not logging user information is an advantage and a disadvantage.
Batch download is not available: Vimeomate has not yet launched the function of batch download.

Website: https://vimeomate.com/

TikFast

As the same series of products as Vimeomate, TikFast has the same advantages as Vimeomate. It can download a variety of videos without watermark and audios from the TikTok platform.

Main advantages:

Simple operation: it only takes 1 minute to easily download a TikTok video and audio;
Free forever: TikFast has no paid plans, all features are free to use;
High adaptability: TikFast can be used on mobile phones, computers and tablets;
Batch download available: TikFast Google extension supports batch download of up to six videos.

Main shortcoming:

Bulk download loading speed is affected by network speed.

Website: https://tikfast.net

Twiclips

Twitch is a game-based streaming platform, and all kinds of exciting competitive moments are recorded in the official Vods. But there is no button to permanently save the video. Hence, Twclips was born as a downloader for downloading Twitch videos.

Main advantages:

Free forever: you can use Twclips without spending any money;
Safe and secure: data and privacy protection;
Convenient operation: Whether it is a website or an extension, users can successfully download a Twitch video in just 1 minute;

Main shortcoming:

Download time is limited: only videos up to 30 minutes can be downloaded

Website:https://twiclips.com/

Pincase

Pinterest users can only download other people’s pictures, not Pins in the form of videos. Pincase can download Pinterest images, GIFs, videos, Story Pins.

In addition to providing download services from the website, Pincase also provides a Pinterest video downloader extension to help you save Pinterest videos or images to your computer more easily.

Main advantages:

Free forever: you can use Pincase without spending any money;
Safe and secure: data and privacy protection;
Convenient operation: Whether it is a website or an extension, users can successfully download a Pincase image or video in just 1 minute;

Main shortcoming:

Unable to view download history: As a registration-free online site and extension, not logging user information is an advantage and a disadvantage.
Batch download is not available: Pincase has not yet launched the function of batch download.

Website: https://pincase.xyz/

DMsave

DMsave is a free Dailymotion video downloader that provides you with the easiest way for Dailymotion video download.

In addition to providing download services from the website, dmsave also provides a Chrome extension to help you download Dailymotion videos to your computer more easily.

Main advantages:

Easy To Use: Only need three steps, Dailymotion video download is no longer difficult by dmsave.
Full Free：DMsave is a Dailymotion video downloader made with love and promised to always be free.
Efficient: DMsave provides a Chrome extension to help you improve the efficiency of Dailymotion video download.

Main shortcoming:

Bulk download loading speed is affected by network speed.

Website：https://dmsave.top/

Why use a video downloader?

The video downloader can download restricted videos, which is convenient for users to watch their favorite videos offline;
The best video downloader is safe and secure without malware and more;
Ensure the quality of the video and ensure that the user can watch the video in the original quality;
Fast download speed and rich download types;
It is suitable for various environments, ensuring that users can solve download problems whether they are using mobile devices or computers.

How to download Pinterest videos, gifs and images?

Abel Harriman — Tue, 01 Mar 2022 10:29:52 +0000

Pinterest is an image sharing and social media service, and it's fair to say that everyone's Pinterest page content is different. Pinterest will display other Pins according to the content direction of the user's search. Generally speaking, Pinterest can only download static pictures.

How do I download other types of information in Pinterest, like Gifs and videos?

Open your Pinterest and select the video you want to download;
Copy the link of the webpage, it is at the top of the browser;
Open Pincase and you will see an area where you can place links;
Paste the link and click “Download Now” ;

Select the download type. That's it, you can download any Pin videos you want through this method.

Use browser extension to download Vimeo videos without download button.

Abel Harriman — Thu, 10 Feb 2022 10:47:54 +0000

To download a video on Vimeo without a download button, here is a quick guide:

Go to vimeomate.com and select an extension type (Chrome/Edge) to install.
Go to vimeo.com and select the Vimeo video you want to download. You can download any Vimeo video that can be played (including Vimeo videos embedded in your personal website)
Select the video quality to complete the download.