<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Real-Time AI Voice Translation, Transcription Aur Meeting Summary - VoicePing 2.0 on VoicePing</title><link>https://voiceping.net/hi-latn/</link><description>Recent content in Real-Time AI Voice Translation, Transcription Aur Meeting Summary - VoicePing 2.0 on VoicePing</description><generator>Hugo</generator><language>hi-Latn</language><lastBuildDate>Wed, 01 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://voiceping.net/hi-latn/index.xml" rel="self" type="application/rss+xml"/><item><title>Pesh Hai VoicePing ASR Model V0.1</title><link>https://voiceping.net/hi-latn/blog/research-voiceping-asr-model-v01-benchmark/</link><pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate><guid>https://voiceping.net/hi-latn/blog/research-voiceping-asr-model-v01-benchmark/</guid><description>&lt;h2 id="pesh-hai-voiceping-asr-model-v01">Pesh Hai VoicePing ASR Model V0.1&lt;/h2>
&lt;p>Aaj hum pesh kar rahe hain &lt;strong>VoicePing ASR Model V0.1&lt;/strong>, hamara multilingual speech-to-text model un bhashaon ke liye jo VoicePing mein sabse zyada istemal hoti hain: &lt;strong>English, Japanese, Korean, Chinese aur Vietnamese&lt;/strong>.&lt;/p>
&lt;p>VoicePing multilingual spoken communication ke ird-gird bana hai: meetings, events, voice translation, transcripts, summaries aur search. In workflows mein ASR (Automatic Speech Recognition, yaani boli gayi baat ko text mein badalna) koi alag-thalag feature nahi hai. Yeh poore product experience ki pehli layer hai. Agar transcript unstable ho, to aage ka har step kam useful ho jaata hai.&lt;/p></description></item><item><title>Emotional TTS Benchmark: Japanese aur Chinese ke liye Qwen3-TTS, CosyVoice, IndexTTS-2, Fish Audio aur VoxCPM</title><link>https://voiceping.net/hi-latn/blog/research-ja-zh-emotional-tts-benchmark/</link><pubDate>Fri, 26 Jun 2026 00:00:00 +0000</pubDate><guid>https://voiceping.net/hi-latn/blog/research-ja-zh-emotional-tts-benchmark/</guid><description>&lt;p>&lt;strong>Models aur references:&lt;/strong>&lt;/p>
&lt;ul>
&lt;li>&lt;a href="https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice">Qwen3-TTS CustomVoice 1.7B&lt;/a>
&lt;/li>
&lt;li>&lt;a href="https://arxiv.org/html/2412.10117v2">CosyVoice 300M Instruct / CosyVoice2&lt;/a>
&lt;/li>
&lt;li>&lt;a href="https://huggingface.co/fishaudio/s1-mini">Fish Audio S1-mini&lt;/a>
&lt;/li>
&lt;li>&lt;a href="https://github.com/OpenBMB/VoxCPM">VoxCPM2&lt;/a>
&lt;/li>
&lt;li>&lt;a href="https://arxiv.org/html/2506.21619v2">IndexTTS-2&lt;/a>
&lt;/li>
&lt;/ul>
&lt;h2 id="saaransh">Saaransh&lt;/h2>
&lt;p>Humne &lt;strong>Japanese aur Chinese&lt;/strong> ke liye paanch emotional TTS systems ko chhe target emotions par benchmark kiya: &lt;code>neutral&lt;/code>, &lt;code>happy&lt;/code>, &lt;code>sad&lt;/code>, &lt;code>angry&lt;/code>, &lt;code>fear&lt;/code>, aur &lt;code>disgust&lt;/code>. Sentences neutral rakhe gaye, taaki emotion text se nahi balki speech style se aaye.&lt;/p>
&lt;p>Sabse balanced candidate &lt;strong>Qwen3-TTS CustomVoice 1.7B&lt;/strong> hai. Isme low CER, best anchor hit rate, strong naturalness, aur Japanese/Chinese ke liye sabse practical emotion balance dikha.&lt;/p></description></item></channel></rss>