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Introducing Groq Whisper Large V3 Turbo: Ultra-Fast, Accurate Speech-to-Text Transcription

Tal Peretz

— 2 min read
Introducing Groq Whisper Large V3 Turbo: Ultra-Fast, Accurate Speech-to-Text Transcription

The latest iteration of Groq's Whisper models, Whisper Large V3 Turbo, has set new standards in speech-to-text transcription, delivering exceptional speed and accuracy, especially in multilingual contexts. Let's explore the key features, performance comparisons, and practical considerations of this powerful new model.

Key Features and Capabilities

  • 216x Real-Time Speed Factor: Whisper Large V3 Turbo outperforms the standard Whisper Large V3, providing significantly faster transcription while maintaining high accuracy.
  • Multilingual Excellence: It achieves top-tier performance with multilingual audio, matching or surpassing comparable models in terms of word error rates (WER).
  • High Accuracy: Benchmark tests show Whisper Large V3 Turbo achieves approximately 1% lower WER compared to other top models.

Performance Comparison

In recent benchmarks, here’s how Whisper Large V3 Turbo compares:

  • Whisper Large V3 Turbo (Groq): 216x real-time speed factor
  • Standard Whisper Large V3 (Groq): 189x real-time speed factor
  • In multilingual tests, Whisper Large V3 Turbo tied for the lowest WER, demonstrating superior accuracy for languages like French and others.

Implementation Example

Here's a quick guide on how to implement Whisper Large V3 Turbo using Hugging Face's Transformers library:


import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
from datasets import Audio, load_dataset

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

model_id = "openai/whisper-large-v3-turbo"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

# Load and process audio
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
sample = dataset[0]["audio"]

inputs = processor(
    sample["array"],
    sampling_rate=sample["sampling_rate"],
    return_tensors="pt",
    truncation=False,
    padding="longest",
    return_attention_mask=True,
)
inputs = inputs.to(device, dtype=torch_dtype)

# Generation parameters
gen_kwargs = {
    "max_new_tokens": 448,
    "num_beams": 1,
    "condition_on_prev_tokens": False,
    "compression_ratio_threshold": 1.35,
    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
    "logprob_threshold": -1.0,
    "no_speech_threshold": 0.6,
    "return_timestamps": True,
}

pred_ids = model.generate(**inputs, **gen_kwargs)
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False)
print(pred_text)

Pricing and Resource Considerations

  • Cost: $11.11 per million seconds transcribed (approximately $0.111 per hour), with zero output cost.
  • Resource Limits: GroqCloud paid users can now handle audio files up to 100MB provided via URL, ideal for longer transcription tasks.

When to Use Groq Whisper Large V3 Turbo

  • High-speed, accurate transcription requirements
  • Multilingual content processing
  • Applications balancing cost-efficiency, accuracy, and speed
  • Production environments requiring rapid, reliable transcription

When Not to Use

  • English-only applications where Groq Distil Whisper offers better cost-efficiency
  • Highly specialized vocabulary or extremely rare languages
  • Low-resource environments or real-time transcription of very lengthy content
  • Applications prioritizing absolute maximum accuracy above all else

Conclusion

Groq Whisper Large V3 Turbo combines speed, accuracy, multilingual capabilities, and cost-effectiveness, making it a top choice for speech-to-text applications in 2025. Evaluate your project needs carefully and leverage this powerful model to enhance transcription workflows effectively.

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