Pesh hai VoicePing ASR Model V0.1 — English, Japanese, Korean, Chinese aur Vietnamese ke liye hamara multilingual speech-to-text model.
Pesh Hai VoicePing ASR Model V0.1
Aaj hum pesh kar rahe hain VoicePing ASR Model V0.1, hamara multilingual speech-to-text model un bhashaon ke liye jo VoicePing mein sabse zyada istemal hoti hain: English, Japanese, Korean, Chinese aur Vietnamese.
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.
VoicePing ASR Model V0.1 isi reality ke liye design kiya gaya hai. Yeh un paanch core bhashaon par focus karta hai jinhe hum sabse zyada serve karte hain, taaki real conversations ke liye zyada saaf transcripts ban saken.
Multilingual Kaam Ke Liye Ek Hi Model
General-purpose speech recognition ne tezi se taraqqi ki hai, lekin real multilingual audio mein aaj bhi kuch mushkil pehlu hain:
- Japanese, Korean aur Chinese ko language-aware text handling chahiye.
- Vietnamese sahi tone marks aur word boundaries par nirbhar karti hai.
- Lambi ya noisy clips mein adhoore transcripts, khaali outputs aur baar-baar repeat hota text saamne aa sakta hai.
- Cloud models alag-alag bhashaon mein alag tarah se behave kar sakte hain, bhale hi API ek jaisi dikhe.
- Jo system kisi public benchmark par accha perform karta hai, woh zaroori nahi ki meetings, events aur voice translation ke liye best fit ho.
VoicePing ASR Model V0.1 is paanch-bhasha product surface ke ird-gird banaya gaya hamara pehla consolidated model hai. Neeche diya gaya benchmark ek practical sawaal poochta hai: yeh us speech ko kitni acchi tarah transcribe karta hai jiski hamare users ko sach mein parwah hai?
Yeh Kya Karta Hai
VoicePing ASR Model V0.1 in bhashaon mein speech transcribe karta hai:
- English
- Japanese
- Korean
- Chinese
- Vietnamese
Iska output wohi transcript hai jo aage VoicePing ke features — jaise translation, captions, meeting notes aur searchable conversation history — ko power karta hai.
Is article mein ASR (Automatic Speech Recognition) aur STT (Speech-to-Text) ek hi matlab mein istemal kiye gaye hain. Dono ka matlab hai speech-to-text transcription.
Evaluation
Dataset
Evaluation ke liye ek multilingual VoicePing speech set istemal kiya gaya hai, jismein har bhasha ki 1,000 clips hain — kul milakar lagbhag 41 ghante ka audio. Yeh clips us tarah ki speech ko reflect karti hain jo VoicePing practice mein handle karta hai: saaf read-aloud recordings nahi, balki real conversations.
| Bhasha | Clips |
|---|---|
| English | 1,000 |
| Japanese | 1,000 |
| Vietnamese | 1,000 |
| Korean | 1,000 |
| Chinese | 1,000 |
| Kul | 5,000 |
Har system ko same audio set par test kiya gaya hai.
Compare Kiye Gaye Models
Hum VoicePing ASR Model V0.1 ki tulna widely used cloud speech systems aur open ASR models se karte hain, jinmein Google Cloud STT, Azure AI Speech, OpenAI transcription models, ElevenLabs Scribe v2, Deepgram Nova-3, Qwen3-ASR aur SenseVoiceSmall shaamil hain.
Scoring
Headline metric hai word error rate (WER): jitna kam, utna behtar. WER yeh maapta hai ki human reference transcript ke muqable kitne shabd insert, delete ya substitute hue.
Latency
Production ASR ke liye sirf accuracy hi kaafi nahi hai. Hum yeh bhi maapte hain ki har system transcript lautane mein kitna samay leta hai, kyunki jo model accurate ho lekin slow, woh live meetings aur events mein phir bhi kharab experience de sakta hai.
Mukhya Natije
Neeche diya gaya chart VoicePing ASR Model V0.1 aur is benchmark ke external speech-to-text systems ke liye paanchon bhashaon ka average word error rate compare karta hai. Chhoti bars behtar hain. Bhasha-war charts results section mein diye gaye hain.
Bhasha Ke Hisaab Se Accuracy
| System | EN WER | JA WER | VI WER | KO WER | ZH WER | Macro WER |
|---|---|---|---|---|---|---|
| VoicePing ASR Model V0.1 | 20.2% | 20.4% | 15.5% | 24.5% | 16.0% | 19.3% |
| Google Cloud STT V1 default | 23.1% | 23.5% | 52.1% | 57.8% | 44.2% | 40.1% |
| Google Cloud STT Chirp 2 | 24.5% | 29.7% | 14.8% | 32.8% | 22.6% | 24.9% |
| Google Cloud STT Chirp 3 | 22.9% | 26.4% | 20.1% | 37.4% | 19.2% | 25.2% |
| Azure AI Speech | 23.0% | 21.1% | 21.0% | 37.3% | 22.5% | 25.0% |
| OpenAI GPT-4o Transcribe | 50.6% | 52.4% | 64.3% | 44.1% | 29.1% | 48.1% |
| OpenAI GPT Realtime Whisper | 31.8% | 26.2% | 20.9% | 33.0% | 22.4% | 26.9% |
| Qwen3-ASR 0.6B | 23.8% | 29.7% | 26.2% | 38.2% | 20.9% | 27.7% |
| Qwen3-ASR 1.7B | 21.9% | 25.0% | 22.0% | 33.1% | 20.0% | 24.4% |
| SenseVoiceSmall | 28.0% | 37.4% | 99.9% | 45.9% | 28.1% | 47.9% |
| ElevenLabs Scribe v2 | 28.6% | 20.3% | 15.4% | 31.5% | 21.2% | 23.4% |
| Deepgram Nova-3 | 29.3% | 28.0% | 38.4% | 44.8% | 29.2% | 34.0% |
Leaderboard
| System | Macro WER | Median latency | Notes |
|---|---|---|---|
| VoicePing ASR Model V0.1 | 19.3% | 1.22s | VoicePing multilingual ASR |
| Google Cloud STT V1 default | 40.1% | 7.47s | Cloud speech-to-text |
| Google Cloud STT Chirp 2 | 24.9% | 7.12s | Cloud speech-to-text |
| Google Cloud STT Chirp 3 | 25.2% | 7.32s | Cloud speech-to-text |
| Azure AI Speech | 25.0% | 7.12s | Cloud speech-to-text |
| OpenAI GPT-4o Transcribe | 48.1% | 1.53s | OpenAI transcription |
| OpenAI GPT Realtime Whisper | 26.9% | 7.17s | OpenAI transcription |
| Qwen3-ASR 0.6B | 27.7% | 3.56s | Open ASR model |
| Qwen3-ASR 1.7B | 24.4% | 4.18s | Open ASR model |
| SenseVoiceSmall | 47.9% | 0.07s | Open ASR model |
| ElevenLabs Scribe v2 | 23.4% | 3.07s | Cloud speech-to-text |
| Deepgram Nova-3 | 34.0% | 1.33s | Cloud speech-to-text |
VoicePing ASR Model V0.1 is benchmark mein sabse kam macro WER ke saath-saath sabse tez median response times mein se ek — 1.22 seconds — bhi deta hai. Is run mein jo systems isse tez respond karte hain, woh iske badle kaafi zyada accuracy chhod dete hain.
Bhasha Ke Hisaab Se Natije
English
Is comparison mein VoicePing ASR Model V0.1 ka English WER sabse kam hai — 20.2% — jo Qwen3-ASR 1.7B aur yahan test kiye gaye pramukh cloud systems se aage hai.
Japanese
Japanese VoicePing ke liye sabse important bhashaon mein se ek hai. Is dataset par VoicePing ASR Model V0.1 20.4% WER tak pahunchta hai — best Japanese result ke liye ElevenLabs Scribe v2 (20.3%) ke saath lagbhag barabari par — aur Azure AI Speech tatha open ASR baselines se aage.
Vietnamese
Vietnamese is benchmark ki sabse kaante ki takkar hai: Google Cloud STT Chirp 2 14.8% WER ke saath aage hai, aur ElevenLabs Scribe v2 (15.4%) tatha VoicePing ASR Model V0.1 (15.5%) theek uske peechhe lagbhag barabari par hain.
Korean
Korean is benchmark ke sabse saaf antaron mein se ek dikhata hai. VoicePing ASR Model V0.1 24.5% WER record karta hai, jo systems ke agle group se kaafi aage hai.
Chinese
Chinese VoicePing ASR Model V0.1 ke liye ek aur strong area hai — 16.0% WER — jismein Google Cloud STT Chirp 3 aur Qwen3-ASR 1.7B sabse kareeb hain.
Humne Kya Seekha
- VoicePing ASR Model V0.1 is benchmark mein sabse strong overall accuracy rakhta hai — paanchon bhashaon mein 19.3% macro WER.
- Natija har bhasha mein ek jaisa nahi hai: is run mein English, Korean aur Chinese mein VoicePing ki badhat sabse saaf dikhti hai, Japanese mein best cloud system ke saath lagbhag barabari hai, aur Vietnamese ek kaante ki takkar hai jo ek point se bhi kam antar se tay hui.
- Bade general-purpose systems product-specific multilingual speech par apne aap nahi jeet jaate.
- User experience ke liye accuracy aur response time dono maayne rakhte hain, khaaskar live meetings aur events mein.
Aage Kya
VoicePing ASR Model V0.1 ek pehla release hai, aur yeh benchmark ek snapshot. Dataset us tarah ki speech se banaya gaya hai jo VoicePing practice mein handle karta hai, isliye yeh hamare product ke liye taiyari maapta hai, kisi universal public benchmark ki jagah nahi leta — aur comparison ke cloud systems evolve hote rahenge, hamara model bhi. Latency deployment environment par bhi depend karti hai, isliye speed numbers ko absolute nahi, indicative maan kar dekhein.
Yahan se hamara kaam un jagahon par focus karta hai jinki taraf yeh evaluation ishara karta hai: har bhasha mein bache hue error patterns ko kam karna, test set ko zyada noisy, lambe aur zyada domain-specific audio ke saath badhana, aur naye speech-to-text systems aane par comparison ko aur broad karna. Automatic scores is kaam ko guide karte hain, aur human transcript review har release decision ka hissa bana rehta hai.
Nishkarsh
VoicePing ASR Model V0.1 English, Japanese, Korean, Chinese aur Vietnamese ke liye hamara pehla consolidated multilingual ASR model hai. Is 5,000-clip benchmark mein yeh test kiye gaye systems mein sabse strong overall accuracy deta hai, aur woh bhi sabse tez response times mein se ek ke saath — aur yahi woh transcription layer hai jis par VoicePing ka har doosra feature banta hai.
Asli badlav focus ka hai: hum ASR ko ek real multilingual communication product ke hisse ke roop mein evaluate kar rahe hain, kisi alag-thalag model demo ke roop mein nahi.
