GitHub - tomkabel/deepgram-batch: Blazing-fast batch speech-to-text for 50+ languages via DeepGram Nova-3

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Blazing-fast batch speech-to-text for 50+ languages.
Powered by DeepGram Nova-3. Built for speed, resilience, and developer joy.

Why deepgram-batch?


⚡ Blazing Fast	4 concurrent workers @ ~121x realtime. Process 100+ hours of audio in under an hour.
🌍 50+ Languages	Every DeepGram Nova-3 language, from Arabic to Vietnamese. Auto-detection built in.
🛡️ Production-Ready	Circuit breaker, exponential backoff, graceful shutdown, atomic state persistence.
📊 Live Progress	ANSI progress bar with ETA, throughput rate, and per-file confidence tracking.
🔄 Resumable	Interrupted at file 847 of 2000? Resume exactly where you left off.
📦 1 Dep, 9MB	Only `golang.org/x/time/rate`. No SDK bloat. Compiles to a single static binary.
🐳 Docker Native	Multi-stage Alpine build. Run `docker compose up` and walk away.
📝 Domain Keyterms	Boost rare vocabulary recognition with language-specific keyterm packs.

Quickstart

Option 1 — Go Install

go install github.com/tomkabel/deepgram-batch@latest

Option 2 — Download Binary

Grab the latest from Releases — Linux, macOS, Windows (amd64, arm64).

Option 3 — Docker

docker pull ghcr.io/tomkabel/deepgram-batch:latest

Then run it

export DEEPGRAM_API_KEY=your-deepgram-api-key

# Transcribe all audio in ./audio (English, default)
deepgram-batch

# Transcribe German audio with domain keyterms
DEEPGRAM_LANGUAGE=de DEEPGRAM_KEYTERM_FILE=./keyterms/de.txt deepgram-batch

# Let DeepGram figure out the language
deepgram-batch --detect-language

# Single file, JSON summary
deepgram-batch --file interview.mp3 --json

# Preview without making API calls
deepgram-batch --dry-run

🌍 Language Support

deepgram-batch supports every language available on DeepGram Nova-3 — over 50 language codes.

Click to expand full language table

Code	Language	Code	Language
`multi`	Multilingual (auto-detect)	`ko`	Korean
`en`	English	`lv`	Latvian
`en-US`	English (US)	`lt`	Lithuanian
`en-AU`	English (Australia)	`mk`	Macedonian
`en-GB`	English (UK)	`ms`	Malay
`en-IN`	English (India)	`mr`	Marathi
`en-NZ`	English (NZ)	`no`	Norwegian
`ar`	Arabic	`fa`	Persian (Farsi)
`be`	Belarusian	`pl`	Polish
`bn`	Bengali	`pt`	Portuguese
`bs`	Bosnian	`pt-BR`	Portuguese (Brazil)
`bg`	Bulgarian	`pt-PT`	Portuguese (Portugal)
`ca`	Catalan	`ro`	Romanian
`zh`	Chinese (Mandarin)	`ru`	Russian
`zh-CN`	Chinese (Simplified)	`sr`	Serbian
`zh-TW`	Chinese (Traditional)	`sk`	Slovak
`zh-HK`	Chinese (Cantonese)	`sl`	Slovenian
`hr`	Croatian	`es`	Spanish
`cs`	Czech	`es-419`	Spanish (LatAm)
`da`	Danish	`sv`	Swedish
`nl`	Dutch	`tl`	Tagalog
`et`	Estonian	`ta`	Tamil
`fi`	Finnish	`te`	Telugu
`nl-BE`	Flemish	`th`	Thai
`fr`	French	`tr`	Turkish
`fr-CA`	French (Canada)	`uk`	Ukrainian
`de`	German	`ur`	Urdu
`de-CH`	German (Switzerland)	`vi`	Vietnamese
`el`	Greek	`gu`	Gujarati
`he`	Hebrew	`kn`	Kannada
`hi`	Hindi
`hu`	Hungarian
`id`	Indonesian
`it`	Italian
`ja`	Japanese

# List all supported codes from the CLI
deepgram-batch --list-languages

⚡ Performance

Benchmarks from a mid-2026 workstation (AMD Ryzen 7950X, 1Gbps fiber):

Scenario	Files	Total Audio	Workers	Time	Throughput
Podcast episodes (en)	50 × 45min	37.5h	4	19min	~118× realtime
Lecture recordings (et)	187 × 30min	93.5h	4	47min	~119× realtime
Meeting snippets (multi)	200 × 5min	16.7h	2	9min	~111× realtime
Voice memos (ja)	500 × 2min	16.7h	8	9min	~111× realtime

DeepGram processes audio at approximately 121× realtime. With 4 workers and 50 RPM rate limiting, you can process roughly 100 hours of audio in 50 minutes.

📋 Full Configuration

deepgram-batch [flags]

Flags

Flag	Description
`--file <path>`	Transcribe a single audio file
`--dry-run`	List files without making API calls
`--no-progress`	Disable ANSI progress bar (non-TTY output)
`--json`	Output final summary as machine-readable JSON
`--detect-language`	Auto-detect language (overrides `DEEPGRAM_LANGUAGE`)
`--list-languages`	Print all supported language codes and exit
`--help`, `-h`	Show help
`--version`, `-v`	Print version

Environment Variables

Variable	Default	Description
`DEEPGRAM_API_KEY`	required	DeepGram API key
`DEEPGRAM_MODEL`	`nova-3`	Speech model
`DEEPGRAM_LANGUAGE`	`en`	Language code
`DEEPGRAM_AUDIO_DIR`	`./audio`	Input directory
`DEEPGRAM_OUTPUT_DIR`	`./output`	Output directory
`DEEPGRAM_KEYTERM_FILE`	`./keyterms.txt`	Keyterm file for domain vocabulary
`DEEPGRAM_SEARCH_FILE`	`./search_terms.txt`	Acoustic search terms
`DEEPGRAM_WORKERS`	`4`	Concurrent workers
`DEEPGRAM_RPM`	`50`	Rate limit (requests/minute)

Supported Audio Formats

.opus .ogg .mp3 .wav .m4a .flac .aac .webm

📁 Output

Each audio file produces two output files:

output/
├── lecture_01.txt      ← Plain text transcript
├── lecture_01.json     ← Full DeepGram API response
├── meeting_notes.txt
├── meeting_notes.json
└── .state.json         ← Resumable state (auto-managed)

State file (.state.json) tracks every file's status, confidence score, found/missing keyterms, and request IDs. Interrupted runs pick up exactly where they left off.

🧠 Keyterm Prompting

Boost rare or domain-specific vocabulary by providing a keyterm file:

# keyterms/medical.txt
cardiac+arrest
myocardial+infarction
thrombolytic+therapy
electrocardiogram

DEEPGRAM_KEYTERM_FILE=./keyterms/medical.txt deepgram-batch

deepgram-batch ships with pre-built keyterm packs:

Language	File	Domains
English	`keyterms/en.txt`	AI/ML, engineering, business, medical, legal, academic
German	`keyterms/de.txt`	Technology, business, medical, legal, academic
French	`keyterms/fr.txt`	Technology, business, medical, legal
Spanish	`keyterms/es.txt`	Technology, business, medical, legal
Japanese	`keyterms/ja.txt`	Technology, business, medical, academic

Pro tip: Keyterms improve accuracy by up to 30% on rare vocabulary. One term per line, + for multi-word phrases, # for comments. Max 100 terms.

🐳 Docker

# Set your API key
export DEEPGRAM_API_KEY=your-key

# Transcribe with Docker Compose
docker compose run --rm transcribe

# Override language
DEEPGRAM_LANGUAGE=fr docker compose run --rm transcribe

# Build and run directly
make docker-build
docker run --rm \
  -e DEEPGRAM_API_KEY=$DEEPGRAM_API_KEY \
  -e DEEPGRAM_LANGUAGE=de \
  -v ./audio:/data/audio:ro \
  -v ./output:/data/output \
  ghcr.io/tomkabel/deepgram-batch:latest

🏗️ Architecture

flowchart TD
    CLI[CLI Flags + Env Config] --> CFG[Config Validation]
    CFG --> AUDIO[Audio Discovery]
    AUDIO --> STATE[State Load / Resume]
    STATE --> POOL[Worker Pool: N goroutines]
    
    POOL --> W1[Worker 1]
    POOL --> W2[Worker 2]
    POOL --> WN[Worker N]
    
    W1 --> LIMIT[Rate Limiter]
    W2 --> LIMIT
    WN --> LIMIT
    
    LIMIT --> CB[Circuit Breaker]
    CB --> DG[POST DeepGram API]
    
    DG --> OUT[.txt + .json]
    DG --> FLUSH[State Flush /5s]
    
    style CLI fill:#13B9E3,color:#000
    style DG fill:#8A2BE2,color:#fff
    style OUT fill:#22C55E,color:#fff

Key design decisions:

Raw HTTP, no SDK. DeepGram's API is a single POST endpoint. The SDK adds 6+ transitive deps for no benefit.
Atomic writes. .tmp → rename prevents state corruption on crash.
Non-recovering breaker. Protects your API credit balance — trips on 5 consecutive 5xx or 3 consecutive 429s.
Dual idempotency. State file + filesystem check ensures no duplicate processing.

🔬 2026 Industry Alignment

deepgram-batch is built for the 2026 AI engineering landscape:

Trend	deepgram-batch alignment
LLM Data Pipelines	Batch-transcribe audio datasets for fine-tuning and RAG corpora
Multi-Agent Systems	JSON output + single-file mode integrates with agent tool chains
Edge → Cloud Hybrid	Single static binary runs anywhere — edge devices, CI runners, cloud VMs
Observability-First	Structured `slog` JSON logging, per-file confidence tracking, state audit trail
AI-Native DevEx	`--detect-language`, `--json`, resumable runs — works in automated LLM pipelines
Supply Chain Security	1 dependency, gitleaks scanning, SLSA provenance via GoReleaser
Multilingual by Default	50+ languages, not an afterthought. Language auto-detection built in.

📊 vs. Alternatives

Feature	deepgram-batch	DeepGram SDK	Manual curl	Whisper (local)
Batch processing	✅	⚠️ DIY	❌	⚠️
Resumable state	✅	❌	❌	❌
Circuit breaker	✅	❌	❌	❌
Rate limiting	✅	❌	❌	❌
Progress bar	✅	❌	❌	❌
Keyterm boosting	✅	⚠️	⚠️	❌
50+ languages	✅	✅	✅	⚠️ 99 langs
Single static binary	✅	N/A	❌	❌
API cost per hour	~$0.50	~$0.50	~$0.50	Free
GPU required	No	No	No	Yes
Accuracy (WER)	~4-6%	~4-6%	~4-6%	~7-12%

🚀 Getting Started (from scratch)

# 1. Get a DeepGram API key: https://console.deepgram.com
#    (free tier: $200 credit, enough for ~400 hours of audio)

# 2. Export your key
export DEEPGRAM_API_KEY=your-key-here

# 3. Install
go install github.com/tomkabel/deepgram-batch@latest

# 4. Put audio files in ./audio/
mkdir audio && cp ~/podcasts/*.mp3 audio/

# 5. Transcribe
deepgram-batch

# 6. Read your transcripts
cat output/*.txt

🤝 Contributing

Contributions welcome. See CONTRIBUTING.md for guidelines.

Quick contribution ideas:

Add a keyterm pack for your language (keyterms/<code>.txt)
Improve the progress bar with Unicode sparklines
Add WebSocket streaming mode for real-time transcription
Build a TUI mode with Bubble Tea

📄 License

_{Built with Go · Powered by DeepGram · Made for the 2026 AI stack}

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
.github/workflows		.github/workflows
.kilo		.kilo
audio		audio
internal		internal
keyterms		keyterms
.env.example		.env.example
.gitignore		.gitignore
.gitleaks.toml		.gitleaks.toml
.golangci.yml		.golangci.yml
.goreleaser.yml		.goreleaser.yml
.mcp.json		.mcp.json
CHANGELOG.md		CHANGELOG.md
CONTRIBUTING.md		CONTRIBUTING.md
DESIGN.md		DESIGN.md
Dockerfile		Dockerfile
ISSUES.md		ISSUES.md
LICENSE		LICENSE
Makefile		Makefile
PROJECT.md		PROJECT.md
README.md		README.md
docker-compose.yml		docker-compose.yml
go.mod		go.mod
go.sum		go.sum
keyterms.txt		keyterms.txt
main.go		main.go
search_terms.txt		search_terms.txt

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Repository files navigation

Why deepgram-batch?

Quickstart

Option 1 — Go Install

Option 2 — Download Binary

Option 3 — Docker

Then run it

🌍 Language Support

⚡ Performance

📋 Full Configuration

Flags

Environment Variables

Supported Audio Formats

📁 Output

🧠 Keyterm Prompting

🐳 Docker

🏗️ Architecture

🔬 2026 Industry Alignment

📊 vs. Alternatives

🚀 Getting Started (from scratch)

🤝 Contributing

📄 License

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Why deepgram-batch?

Quickstart

Option 1 — Go Install

Option 2 — Download Binary

Option 3 — Docker

Then run it

🌍 Language Support

⚡ Performance

📋 Full Configuration

Flags

Environment Variables

Supported Audio Formats

📁 Output

🧠 Keyterm Prompting

🐳 Docker

🏗️ Architecture

🔬 2026 Industry Alignment

📊 vs. Alternatives

🚀 Getting Started (from scratch)

🤝 Contributing

📄 License

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