sorika-labs/vaani2-flash

⚡ Vaani-2 Flash ASR

High-Throughput, Low-Latency Speech Foundation Model for Production APIs & Real-Time Streaming

Developed by Sorika Labs

Hugging Face Model • Sorika Labs Organization

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🌟 Overview & Key Breakthroughs

Vaani-2 Flash is Sorika Labs' flagship high-throughput speech recognition foundation model. Built on top of OpenAI's 809M-parameter whisper-large-v3-turbo, Vaani-2 Flash delivers an unmatched combination of extreme inference velocity (up to 30.3x Real-Time) and flawless English and Indian recognition accuracy.

In our comprehensive 10-benchmark evaluation, Vaani-2 Flash achieved the #1 lowest English Average WER (5.90%) across all evaluated models, beating both Alibaba's Qwen3-ASR (6.19%) and OpenAI Whisper baselines (8.05%).

Key Highlights:

• 🏆 #1 Overall English Accuracy (5.90% Avg WER):
  • VoxPopuli: 2.22% WER (Rank 1)
  • SPGISpeech: 3.55% WER (Rank 1)
  • Earnings22: 8.62% WER (Rank 1)
  • LibriSpeech Clean: 4.84% WER
• ⚡ Blazing Inference Velocity (21.5x Real-Time): Thanks to the optimized 4-layer decoder of Whisper-Turbo, Vaani-2 Flash processes 30 seconds of audio in under 1.0 second on GPU, making it ideal for live streaming and enterprise APIs.
• 🇮🇳 Robust Indian Speech Recognition: Achieves 16.38% on FLEURS Hindi and 14.44% on Private Conversational Hindi, slashing Whisper-Small's 47.8% error rate by more than 60%.
• 🛡️ Zero Catastrophic Forgetting: Preserves global multilingual capabilities across European and Asian languages without weight drift.

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🧭 Sorika Labs Vaani Speech Series Portfolio

The Vaani family is engineered by Sorika Labs to provide production-ready speech intelligence across diverse computational constraints:

Series	Model Name	Backbone	Params	Primary Strength	Recommended Use Case
Lite Series	vaani1-lite-asr	Whisper-Small	244M	Ultra-lightweight, 99+ global languages	Edge devices, mobile apps, low-latency CPU
Lite Series	vaani1.1-lite-asr	Qwen3-ASR	600M	SOTA Hindi accuracy (11.78% WER), LLM-grade reasoning	High-accuracy transcription, noisy audio, Devanagari purity
Flash Series	vaani2-flash	Whisper-Turbo	809M	#1 English WER (5.90%), 21.5x Real-Time speed	High-throughput production APIs, real-time live streaming
Flash Series	vaani2.1-flash (Coming Soon)	Whisper-Turbo	809M	Curated Hindi + Hinglish fine-tune targeting <10% WER	Ultra-fast bilingual enterprise applications
Pro Series	Vaani-Pro (Research)	Dense Foundation	1.5B+	Long-form context, speaker diarization, multi-dialect	Complex enterprise contact centers & broadcasts

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📊 Grand Benchmark Leaderboard (10 Standardized Benchmarks)

All models were evaluated under identical real-world conditions across 7 English benchmarks (meeting, financial, podcast, clean read, noisy speech, parliamentary) and 3 Indic/Hindi benchmarks:

Benchmark Dataset	Domain / Category	OpenAI Whisper-Small (244M)	Vaani-1 Lite (244M)	Vaani-2 Flash (809M)	Alibaba Qwen3-Base (600M)	Vaani-1.1 Lite (600M)	Vaani-2 Speed
AMI-Cleaned	English (Meeting Speech)	12.74%	12.99%	10.41%	9.05%	15.13%	29.4x RTF ⚡
Earnings22	English (Financial Calls)	8.62%	9.90%	8.62% 🥇	10.99%	10.50%	30.3x RTF ⚡
GigaSpeech	English (Podcasts / Books)	8.90%	8.43%	7.28%	6.77%	7.58%	19.1x RTF ⚡
LibriSpeech Clean	English (Clean Read)	5.63%	9.67%	4.84%	4.49%	4.75%	15.9x RTF ⚡
LibriSpeech Other	English (Noisy / Accents)	11.30%	8.70%	4.35%	3.93%	3.60% 🥇	15.3x RTF ⚡
SPGISpeech	English (Financial Disclosures)	3.89%	5.41%	3.55% 🥇	4.74%	5.58%	18.9x RTF ⚡
VoxPopuli	English (Parliamentary / Live)	5.29%	9.20%	2.22% 🥇	3.38%	8.99%	28.1x RTF ⚡
Kathbath (Hindi)	Hindi (Indic Crowdsourced)	50.00%	16.29%	22.47%	14.61%	12.36% 🥇	5.9x RTF
FLEURS (Hindi)	Hindi (Speech-to-Text)	48.88%	25.56%	16.38%	14.39%	14.64%	8.5x RTF
Vaani Private (Hindi)	Hindi (Conversational Real)	44.44%	16.11%	14.44%	7.78%	8.33%	6.8x RTF
English Average WER	7 Datasets	8.05%	9.18%	5.90% 🏆	6.19%	8.02%	21.5x Avg 🚀
Hindi Average WER	3 Datasets	47.77%	19.32%	17.76%	12.26%	11.78% 🏆	7.1x Avg
Overall Average WER	All 10 Datasets	19.97%	12.23%	9.46%	8.01%	9.15%	17.2x Avg

Lower WER indicates superior recognition accuracy. Speed is measured in Real-Time Factor (RTFx) on NVIDIA GPU.

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🚀 Quickstart & Inference

1. Using Hugging Face pipeline

import torch
from transformers import pipeline

device = "cuda:0" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipe = pipeline(
    "automatic-speech-recognition",
    model="sorika-labs/vaani2-flash",
    torch_dtype=dtype,
    device=device,
    chunk_length_s=30,
    trust_remote_code=True
)

# 1. Transcribe English Audio (Ultra-Accurate 5.90% Avg WER)
result_en = pipe("english_call.wav", generate_kwargs={"language": "en", "task": "transcribe"})
print("English:", result_en["text"])

# 2. Transcribe Hindi Audio
result_hi = pipe("hindi_audio.wav", generate_kwargs={"language": "hi", "task": "transcribe"})
print("Hindi:", result_hi["text"])

# 3. Automatic Language Detection & Transcription
result_auto = pipe("mixed_speech.mp3", generate_kwargs={"task": "transcribe"})
print("Transcription:", result_auto["text"])

2. Using AutoModelForSpeechSeq2Seq & AutoProcessor

import torch
import torchaudio
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor

model_id = "sorika-labs/vaani2-flash"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id,
    torch_dtype=dtype,
    low_cpu_mem_usage=True,
    trust_remote_code=True
).to(device)

# Load 16 kHz Mono Audio
audio, sr = torchaudio.load("sample.wav")
if sr != 16000:
    audio = torchaudio.transforms.Resample(sr, 16000)(audio)

inputs = processor(audio.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
input_features = inputs.input_features.to(device, dtype=dtype)

with torch.no_grad():
    predicted_ids = model.generate(input_features, language="hi", task="transcribe")

transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print("Transcription:", transcription[0])

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🔮 Roadmap: Vaani-2.1 Flash

Sorika Labs is actively fine-tuning the next revision, vaani2.1-flash, which pairs the blazing 25x+ RTF Whisper Turbo architecture with a high-fidelity curated Hindi & Hinglish conversational corpus. Target goals:

• Sub-10% Hindi Average WER
• Native Hinglish code-switching normalization
• Retaining the #1 English benchmark ranking

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⚙️ Architecture & Model Specs

• Base Model: openai/whisper-large-v3-turbo (809 Million Parameters)
• Audio Encoder: 32-layer Transformer, 1280 hidden size, 128 mel bins
• Decoder: 4-layer fast Transformer decoder (4x faster than standard large-v3)
• Weights: 100% permanently fused model.safetensors (1.62 GB FP16)
• License: Apache 2.0

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🏢 Developed by Sorika Labs

Sorika Labs — Advancing open-source speech AI for Indian and global enterprises.