Speech to Text API Guide: Comparing Top Options in 2026 and Integration Best Practices

The integration of speech to text capabilities with applications has evolved from a "nice to have" feature to core functionality for many products. From meeting transcription and voice assistants, to video subtitles, call center analysis, and accessibility features, many critical use cases depend on a reliable speech to text API.

This guide is written for developers and technical decision-makers. We compare leading speech-to-text APIs across technical specs, pricing models, and developer experience, and include integration code examples.

6 Key Factors When Choosing a Speech to Text API

When evaluating STT APIs, these following 6 dimensions matter most:

1. Accuracy

WER (Word Error Rate) is the standard metric for measuring accuracy. While leading APIs often achieve WERs below5% on benchmark datasets,real-world performance is what ultimately matters, especially in the presence of noise, accents and domain-specific terms.

2. Latency

In terms of latency, two modes should be evaluated separately:

• Batch mode: Upload complete audio, and receive a complete transcript. Latency is measured as the ratio of processing time to audio duration.
• Streaming mode: Real-time audio transmission with live transcription. Latency is measured by time-to-first-byte and end-to-end delay.

3. Language Support

Key considerations include how many languages the API supports and how effectively it handles mixed-language content, such as code-switching between English and Spanish. Besides, support for dialects and accents should also be taken into account.

4. Feature Set

Whether features like speaker diarization, timestamps, punctuation, word-level confidence scores, custom vocabulary and profanity filtering are supported.

5. Pricing Model

Charged by audio duration or by request volume? Free tier available? Volume discounts offered?

6. Developer Experience

Documentation quality, SDK availability, error handling clarity, and support responsiveness.

Speech to Text API Comparison

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#1 Fish Audio API: The Developer-Friendly All-Rounder

Fish Audio is known for its top-tier TTS capabilities, but its Speech to Text API is equally impressive. Designed with developers in mind, it ranks among the top providers with respect to accuracy, latency, and feature completeness.

Core Technical Specs

Accuracy

Fish Audio's STT API achieves approximately 4.5% WER on standard benchmarks, placing it among industry leaders. More importantly, it could maintain consistent performance even under challenging conditions:

Many APIs perform well under ideal conditions but degrade sharply in the presence of noise or mixed-language input. Fish Audio's consistency is a core strength.

Latency

Fish Audio API supports two modes:

• Batch mode: Processing speed runs at approximately 0.3-0.5x the audio duration, with a10-minute recording typically completing in 3-5 minutes.
• Streaming mode: Time-to-first-byte is around 200-300ms, with end-to-end latency in the range of 500-800ms, making it well-suited for real-time transcription.

Language Support

Supports 50+ languages, covering all major global languages. The standout feature is mixed-language handling–the code-switching processes, such as English-Mandarin and English-Japanese can be completed naturally without recognition breaks.

Feature Deep Dive

Speaker Diarization

The API automatically identifies and labels different speakers. Each output segment is assigned a speaker ID, which can be mapped to actual names at the application layer.

{

"segments": [

{

"speaker": "speaker_1",

"start": 0.0,

"end": 3.2,

"text": "Let's discuss the project timeline today."

},

{

"speaker": "speaker_2",

"start": 3.5,

"end": 6.8,

"text": "Sure, I'll start with an update from the dev team."

}

]

}

Timestamps

Supports both sentence-level and word-level timestamps. For subtitle generation, word-level timestamps can enable word-by-word highlighting effects.

Punctuation and Formatting

Automatically inserts punctuation and intelligently formats entities like numbers, dates, and currencies. For example, "March fifteenth at two pm" is converted to "March 15th at 2:00 PM."

Custom Vocabulary

You can upload custom vocabulary lists to improve recognition accuracy for technical terms, brand names, and proper nouns. This function is particularly useful for vertical applications in healthcare, legal, and finance.

API Integration Examples

Python Batch Example

import requests

API_KEY = "your_api_key"

API_URL = "https://api.fish.audio/v1/speech-to-text"

# Upload audio file for transcription

with open("meeting_recording.mp3", "rb") as audio_file:

response = requests.post(

API_URL,

headers={

  "Authorization": f"Bearer {API_KEY}",

  "Content-Type": "audio/mpeg"

},

data=audio_file,

params={

  "language": "en",

  "speaker_diarization": True,

  "punctuation": True,

  "timestamps": "word"

}

)

result = response.json()

print(result["text"])

Python Streaming Example

import websocket

import json

API_KEY = "your_api_key"

WS_URL = "wss://api.fish.audio/v1/speech-to-text/stream"

def on_message(ws, message):

data = json.loads(message)

if data["type"] == "partial":

print(f"[Live] {data['text']}", end="\r")

elif data["type"] == "final":

print(f"[Final] {data['text']}")

def on_open(ws):

# Send audio data

with open("audio_chunk.wav", "rb") as f:

ws.send(f.read(), opcode=websocket.ABNF.OPCODE_BINARY)

ws.send(json.dumps({"type": "end"}))

ws = websocket.WebSocketApp(

f"{WS_URL}?api_key={API_KEY}&language=en",

on_message=on_message,

on_open=on_open

)

ws.run_forever()

JavaScript/Node.js Example

const fetch = require('node-fetch');

const fs = require('fs');

const API_KEY = 'your_api_key';

const API_URL = 'https://api.fish.audio/v1/speech-to-text';

async function transcribe(audioPath) {

const audioBuffer = fs.readFileSync(audioPath);

const response = await fetch(API_URL, {

method: 'POST',

headers: {

'Authorization': Bearer ${API_KEY},

'Content-Type': 'audio/mpeg'

},

body: audioBuffer

});

const result = await response.json();

return result.text;

}

transcribe('meeting.mp3').then(console.log);

The Unified Advantage: STT + TTS Workflow

Fish Audio's unique value lies in offering both STT and TTS APIs on one platform. This allows you to build complete voice processing pipelines in one place, such as:

• Speech translation: STT transcription → text translation → TTS generates audio in the target language
• Meeting summaries: STT transcription → text summarization → TTS generates an audio briefing
• Content repurposing: STT extracts podcast text → content editing and refinement → TTS generates multilingual audio versions

Both APIs share the same authentication system and billing account, reducing development and operational costs.

Pricing

Fish Audio API adopts a usage-based pricing model. Check the pricing page for current rates. A free tier is available for testing, with volume discounts offered for larger usage volumes.

Documentation and Support

Fish Audio API documentation is well-organized, including:

• A quick start guide
• API reference covering all endpoints and parameters
• Code examples (Python, JavaScript, cURL)
• Error code explanations
• Best practice recommendations

Other Leading APIs: Quick Comparison

OpenAI Whisper API

The OpenAI Whisper API is a cloud-based service built on the Whisper model.

Strengths: High accuracy, solid multilingual support, and competitive pricing ($0.006/min).

Limitations: No streaming support (batch only), no speaker diarization, and a relatively basic feature set.

Best for: Batch transcription scenarios where real-time processing isn't required.

Google Cloud Speech-to-Text

Google Cloud Speech-to-Text is an enterprise-grade STT service, with stability and scalability as its core selling points.

Strengths: Support for 125+ languages, both streaming and batch processing, and enterprise SLA.

Limitations: Complex configuration, unintuitive pricing (charged per 15-second increment), and less attractive to smaller developers.

Best for: Enterprises extensively leveraging the Google Cloud ecosystem, and large-scale applications requiring high availability.

Microsoft Azure Speech

Microsoft's speech service, deeply integrated with the Azure ecosystem.

Strengths: Support for custom model training, enterprise-grade security compliance, and competitive pricing for batch processing.

Limitations: Advantages diminish outside the Azure ecosystem, and the documentation organization can be confusing.

Best for: Enterprises already on Azure, and scenarios requiring custom speech models.

AWS Transcribe

Amazon's transcription service, integrated with the AWS ecosystem.

Strengths: Support for multiple audio formats and seamless integration with S3, Lambda, and other AWS services.

Limitations: Pricing is relatively higher ($0.024/min), with accuracy that isn't top-tier.

Best for: Teams already operating in the AWS ecosystem who require integration with other AWS services.

AssemblyAI

An independent speech AI provider that's grown rapidly in recent years.

Strengths: High accuracy, rich features (summarization, sentiment analysis, content moderation), and a modern API design.

Limitations: Per-second pricing ($0.002/sec = $0.12/min) makes longer audio expensive.

Best for: Scenarios needing speech analysis add-ons, and teams with larger budgets.

Decision Tree for Choosing Your Speech to Text API

Need real-time/streaming transcription?

├─ Yes → Fish Audio / Google Cloud / Azure / AssemblyAI

└─ No → All options viable

Need speaker diarization?

├─ Yes → Fish Audio / Google Cloud / Azure / AWS / AssemblyAI

└─ No → Consider Whisper API (lower cost)

Need mixed-language support?

├─ Yes → Fish Audio (strongest mixed-language handling capability)

└─ No → Choose based on other factors

Already locked into a cloud platform?

├─ Google Cloud → Google Cloud STT

├─ Azure → Azure Speech

├─ AWS → AWS Transcribe

└─ None → Fish Audio / AssemblyAI / Whisper API

Need unified STT + TTS?

├─ Yes → Fish Audio (the only platform offering top-tier quality for both SST and TTS)

└─ No → Choose based on other factors

Integration Best Practices

1. Audio Preprocessing

Preprocessing audio before sending it to the API can improve accuracy:

• Sample rate: 16kHz or higher
• Channels: Mono typically works better than stereo (unless you need to distinguish speakers by channel)
• Format: Most APIs support MP3, WAV, and FLAC. WAV offers lossless quality but results in large files, whereas MP3 provides a good balance between quality and size.
• Noise reduction: If background noise is noticeable, consider applying noise reduction during preprocessing.

2. Error Handling

STT APIs can fail due to network issues, audio quality problems, or server load. Implement:

• Retry logic: Exponential backoff (1s, 2s, 4s...)
• Timeouts: Set reasonable timeouts for batch processing (e.g., twice the audio duration)
• Fallback: Switch to a backup API if the primary one is unavailable

3. Cost Control

• Choose the right mode: Use batch processing when you don't need real-time results (usually cheaper)
• Compress audio: Compress audio within acceptable quality loss to reduce transfer and processing costs
• Cache results: Avoid retranscribing the same audio

4. Privacy and Compliance

• Data transmission: Ensure encrypted transmission via HTTPS/WSS
• Data retention: Understand the API provider's data retention policy
• Sensitive content: For healthcare, legal, and other sensitive content, choose services with compliance certifications

Conclusion

Choosing a proper speech to text API requires balancing accuracy, latency, language support, features, pricing, and developer experience.

For most developers and technical teams, Fish Audio API is a highly recommended choice in 2026. Ranking among the top inaccuracy and latency, it offers outstanding mixed-language handling capabilities, provides a complete feature set (including speaker diarization, timestamps, and custom vocabulary), and delivers unique value through its unified STT and TTS platform.

If you have deeply invested in a specific cloud platform (Google/Azure/AWS), using the STT service of that platform can reduce integration costs. If you only need basic batch transcription without real-time requirements, OpenAI Whisper API offers solid value.

Test a few options using free tiers with real audio from your actual use case before making a final decision.