Ever spent hours cleaning up a transcript? Inserting commas, capitalizing words, adjusting timestamps, and fixing numbers spoken as “twenty-two thousand three hundred ten” rather than “22,310”? I was tired of cloud-based speech recognition tools that compromised privacy and desktop solutions that delivered flat, unpunctuated text without timestamps.

So I tried Parakeet-TDT.

TL;DR

Most speech-to-text tools miss key elements like punctuation, timestamps, or rely on cloud APIs. This blog showcases a fully local transcription system using NVIDIA’s Parakeet-TDT 0.6B model.

✅ Auto punctuation & capitalization
✅ Word/segment-level timestamps
✅ Long audio support
✅ Tested on financial news, lyrics, and tech conversations
✅ Built using Streamlit + NeMo — runs 100% offline

🎯 The Problem: ASR That Misses the Metadata

Most ASR tools do a decent job with basic transcripts. But they fall short when real-world applications demand:

📈 Business number accuracy
🧾 Structured formatting
🔐 Local processing with privacy
🎬 Subtitle alignment

Whether you’re handling earnings calls, voice notes, or executive interviews, flat transcripts won’t cut it.

💡 The Solution: NVIDIA Parakeet-TDT 0.6B

🎥 Live Demo
Watch Parakeet transcribe business audio, lyrics, and interviews — entirely offline:

A full walkthrough of the local ASR system built with Parakeet-TDT 0.6B. Includes architecture overview and transcription demos for financial news, song lyrics, and a tech dialogue.

Note: The lyrics demo segment (Wavin’ Flag) has been muted to comply with copyright restrictions on YouTube.

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⚙️ Key Features

• Auto punctuation & casing

• Word and segment-level timestamps

• Handles long audio (up to 24 mins per chunk)

• CUDA-accelerated

• Free for commercial use (CC-BY-4.0)

• Fast: RTFx 3380 (~56 min of audio/sec at batch size 128)

🧠 Under the Hood: Architecture & Training

📐 Architecture

FastConformer encoder + TDT decoder
600M parameters
Trained on over 120K hours

🧪 Training Overview

• Pretrained with wav2vec on LibriLight

• Fine-tuned on 500 hours of clean speech

• Final training on YouTube-like public datasets

• Trained using NVIDIA NeMo on 64× A100 GPUs

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💻 Setup: Run It Locally (Windows)

The code, requirements, and sample audio files are available on GitHub:
🔗 GitHub — SridharSampath/parakeet-asr-demo

1. Create Conda Environment

create -n parakeet-asr python=3.10 -y
conda activate parakeet-asr

2. Install Dependencies

pip install -r requirements.txt

Includes NeMo, PyTorch, Streamlit, and audio libraries.

3. Install FFmpeg

choco install ffmpeg

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🧠 Code Walkthrough

🔌 Load the Model

model = ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2")
model = model.to("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    model = model.to(torch.bfloat16)

🎧 Audio Preprocessing

audio = AudioSegment.from_file(audio_path)
audio = audio.set_frame_rate(16000).set_channels(1)
audio.export("processed.wav", format="wav")

📝 Transcription

output = model.transcribe([processed_path], timestamps=True)
for seg in output[0].timestamp["segment"]:
    print(f"{seg['start']}s - {seg['end']}s: {seg['segment']}")

Streamlit handles exporting to .csv, .srt, and .txt.

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🖥️ Application Interface — Local ASR in Action

System runs fully offline, loads the 600M model in seconds, and transcribes a 2:37 clip in under 2 seconds on CUDA.

🧪 Real-World Transcription Tests

1. Stock Market News (2:30 mins)

🎧 File: Stockmarketnews.wav
Simulates a financial update with spoken numbers, companies, and currencies.

Transcription wins:

• Phrases like “The Nifty 50 closed at 22,310 points”

• Correct formatting for “₹3,487” and percentage figures

• Accurate punctuation and clarity

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2. Song Lyrics — Wavin’ Flag (3:40 mins)

🎧 File: Wavin-Flag-song.wav
Focuses on lyric structure and repetition.

Transcription wins:

• Captures phrasing: “When I get older, I will be stronger…”

• Punctuation preserves rhythm

• Line breaks and structure detected

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3. Tech Dialogue — Satya x Jensen (5:00 mins)

🎧 File: JensenHuang-SatyaNadella-Conference-talk.wav
First 5 minutes of a Build Conference chat on AI.

Transcription wins:

• Captures phrases like “tokens per dollar per watt”

• Maintains sentence integrity and structure

• Handles longer, multi-speaker content

🧾 Sample Audio Files

• JensenHuang-SatyaNadella-Conference-talk.wav

• Stockmarketnews.wav

• Wavin-Flag-song.wav

Available in the GitHub repo

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📊 Parakeet vs Whisper (Medium)

🏆 Benchmark Leadership

Parakeet ranks #1 on Hugging Face Open ASR Leaderboard (as of May 2025):

• WER: 6.05% (best open model)

• RTFx: 3386

• License: CC-BY-4.0

⚠️ Limitations

• English-only

• Requires GPU (CUDA) for optimal performance

• No built-in speaker diarization

🧠 Final Thoughts

Parakeet-TDT 0.6B offers a strong open-source alternative to Whisper for English transcription — especially when speed, timestamps, and offline processing are critical.

Perfect for:

• Executive interviews

• Financial transcription

• Subtitles & media apps

• Research projects

⚙️ Test Environment

• GPU: NVIDIA RTX 3050 Laptop GPU

• CUDA: 11.8

• OS: Windows 11

• Frameworks: NeMo + PyTorch

🔗 Resources

• Parakeet-TDT 0.6B on Hugging Face

• NeMo Toolkit Documentation

• Hugging Face ASR Leaderboard

• GitHub — parakeet-asr-demo

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🙌 Let’s Connect

If you're exploring ASR, real-time transcription, or multimodal RAG — I'd love to connect:

• LinkedIn — Sridhar Sampath

• GitHub