I remember the first time I built a voice-enabled application from scratch — it took me three weeks to wrangle together different APIs, debug authentication errors, and figure out why my audio files kept getting rejected. That was before I discovered HolySheep AI, which consolidated everything into one unified platform with <50ms latency and a rate that saves you 85%+ compared to ¥7.3 per dollar. In this tutorial, I am going to walk you through every step of integrating Whisper for speech-to-text transcription and TTS for text-to-speech synthesis, using real code you can copy-paste and run today.

What You Will Build By the End of This Tutorial

By the time you finish reading, you will have two fully functional Python scripts:

Prerequisites

Before we dive into the code, make sure you have:

Understanding the HolySheep Voice API Architecture

HolySheep AI provides a unified API endpoint for all voice operations. The base URL is:

https://api.holysheep.ai/v1

All requests require your API key in the header. This is different from OpenAI or Anthropic endpoints — HolySheep consolidates both transcription and synthesis under one roof, meaning you manage one API key for all voice operations.

Part 1: Whisper Speech-to-Text Transcription

How Whisper Transcription Works

Whisper is OpenAI's open-source transcription model. When you send an audio file to the Whisper API, it processes the audio through a neural network trained on millions of hours of multilingual speech data. The model outputs timestamped text segments, language detection, and optional translation capabilities.

Complete Whisper Transcription Code

#!/usr/bin/env python3
"""
HolySheep AI - Whisper Speech-to-Text Transcription
====================================================
This script transcribes audio files to text using the Whisper model.
No Chinese characters in code - all English instructions.
"""

import requests
import json
import os
from pathlib import Path


HolySheep AI Configuration

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your actual key

def transcribe_audio(audio_file_path, language=None, prompt=None):
    """
    Transcribe an audio file to text using Whisper.
    
    Args:
        audio_file_path (str): Path to the audio file (MP3, WAV, M4A, OGG)
        language (str): Optional ISO 639-1 language code (e.g., "en", "zh", "es")
        prompt (str): Optional context to improve transcription accuracy
    
    Returns:
        dict: Transcription result with text, segments, and metadata
    """
    # Validate file exists
    if not os.path.exists(audio_file_path):
        raise FileNotFoundError(f"Audio file not found: {audio_file_path}")
    
    # Prepare the file for upload
    file_extension = Path(audio_file_path).suffix.lower()
    mime_types = {
        '.mp3': 'audio/mpeg',
        '.wav': 'audio/wav',
        '.m4a': 'audio/mp4',
        '.ogg': 'audio/ogg',
        '.flac': 'audio/flac'
    }
    mime_type = mime_types.get(file_extension, 'audio/mpeg')
    
    with open(audio_file_path, 'rb') as audio_file:
        files = {
            'file': (os.path.basename(audio_file_path), audio_file, mime_type)
        }
        
        # Build request data
        data = {}
        if language:
            data['language'] = language
        if prompt:
            data['prompt'] = prompt
        
        # Set headers with API key
        headers = {
            'Authorization': f'Bearer {API_KEY}'
        }
        
        # Make the transcription request
        response = requests.post(
            f"{BASE_URL}/audio/transcriptions",
            files=files,
            data=data,
            headers=headers
        )
        
        # Handle response
        if response.status_code == 200:
            return response.json()
        elif response.status_code == 401:
            raise AuthenticationError("Invalid API key. Check your HolySheep AI credentials.")
        elif response.status_code == 413:
            raise FileSizeError("Audio file too large. Maximum size is 25MB.")
        elif response.status_code == 422:
            raise ValidationError(f"Invalid audio format or parameters: {response.text}")
        else:
            raise APIError(f"Transcription failed with status {response.status_code}: {response.text}")


class TranscriptionError(Exception):
    """Base exception for transcription errors"""
    pass

class AuthenticationError(TranscriptionError):
    """Raised when API authentication fails"""
    pass

class FileSizeError(TranscriptionError):
    """Raised when audio file exceeds size limit"""
    pass

class ValidationError(TranscriptionError):
    """Raised when request validation fails"""
    pass

class APIError(TranscriptionError):
    """Raised for general API errors"""
    pass



Example usage

if __name__ == "__main__":
    # Replace with your actual audio file path
    AUDIO_FILE = "test_audio.mp3"
    
    try:
        print("Starting Whisper transcription via HolySheep AI...")
        print(f"Processing file: {AUDIO_FILE}")
        
        result = transcribe_audio(
            audio_file_path=AUDIO_FILE,
            language="en",  # Set to None for auto-detection
            prompt="This is a technical tutorial about AI APIs."  # Optional context
        )
        
        print("\n" + "="*60)
        print("TRANSCRIPTION RESULT")
        print("="*60)
        print(f"Text: {result.get('text', 'No text returned')}")
        print(f"Language: {result.get('language', 'Not specified')}")
        print(f"Duration: {result.get('duration', 'N/A')} seconds")
        
        if 'segments' in result:
            print(f"\nSegments ({len(result['segments'])} total):")
            for i, segment in enumerate(result['segments'][:3]):  # Show first 3
                print(f"  [{i+1}] {segment.get('text', '')}")
        
        # Save to file
        output_file = "transcription_result.json"
        with open(output_file, 'w') as f:
            json.dump(result, f, indent=2)
        print(f"\nFull result saved to: {output_file}")
        
    except FileNotFoundError as e:
        print(f"File error: {e}")
        print("Tip: Make sure the audio file exists in the same directory as this script.")
    except TranscriptionError as e:
        print(f"Transcription error: {e}")

Testing Your Transcription Setup

Before running the script, create a test audio file or download a sample. You can use any short MP3 file. Save it as test_audio.mp3 in the same directory as your Python script.

Run the script:

python whisper_transcribe.py

If successful, you will see output like:

Starting Whisper transcription via HolySheep AI...
Processing file: test_audio.mp3

============================================================
TRANSCRIPTION RESULT
============================================================
Text: This is a sample transcription of the audio file.
Language: en
Duration: 3.5 seconds

Segments (2 total):
  [1] This is a sample transcription
  [2] of the audio file.

Full result saved to: transcription_result.json

Part 2: TTS Text-to-Speech Synthesis

How TTS Synthesis Works

Text-to-Speech synthesis converts written text into spoken audio. The HolySheep AI TTS endpoint supports multiple voices, adjustable speaking rates, and multiple output formats including MP3 and WAV.

Complete TTS Synthesis Code

#!/usr/bin/env python3
"""
HolySheep AI - Text-to-Speech (TTS) Synthesis
==============================================
This script converts text to natural-sounding speech audio.
"""

import requests
import os
import base64


HolySheep AI Configuration

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Replace with your actual key

def synthesize_speech(
    text,
    voice="alloy",  # Options: alloy, echo, fable, onyx, nova, shimmer
    model="tts-1",   # Options: tts-1 (standard), tts-1-hd (high quality)
    response_format="mp3",  # Options: mp3, wav, opus, aac
    speed=1.0  # Range: 0.25 to 4.0
):
    """
    Convert text to speech audio using HolySheep AI TTS.
    
    Args:
        text (str): The text to synthesize into speech
        voice (str): Voice name for synthesis
        model (str): TTS model to use
        response_format (str): Audio output format
        speed (float): Speech speed multiplier
    
    Returns:
        bytes: Raw audio data
    """
    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json'
    }
    
    payload = {
        'model': model,
        'input': text,
        'voice': voice,
        'response_format': response_format,
        'speed': speed
    }
    
    response = requests.post(
        f"{BASE_URL}/audio/speech",
        headers=headers,
        json=payload
    )
    
    if response.status_code == 200:
        return response.content
    elif response.status_code == 400:
        raise ValidationError(f"Invalid request parameters: {response.text}")
    elif response.status_code == 401:
        raise AuthenticationError("Invalid API key")
    elif response.status_code == 429:
        raise RateLimitError("Rate limit exceeded. Wait before retrying.")
    else:
        raise APIError(f"TTS synthesis failed: {response.status_code} - {response.text}")


def synthesize_and_save(text, output_filename, **kwargs):
    """
    Synthesize speech and save directly to file.
    """
    audio_data = synthesize_speech(text, **kwargs)
    
    with open(output_filename, 'wb') as f:
        f.write(audio_data)
    
    file_size = os.path.getsize(output_filename)
    print(f"Audio saved to: {output_filename}")
    print(f"File size: {file_size:,} bytes ({file_size/1024:.2f} KB)")
    return output_filename


class TTSError(Exception):
    """Base exception for TTS errors"""
    pass

class ValidationError(TTSError):
    """Raised when request validation fails"""
    pass

class AuthenticationError(TTSError):
    """Raised when API authentication fails"""
    pass

class RateLimitError(TTSError):
    """Raised when rate limit is exceeded"""
    pass

class APIError(TTSError):
    """Raised for general API errors"""
    pass



Example usage

if __name__ == "__main__":
    sample_text = """
    Welcome to the HolySheep AI voice synthesis demo. This technology allows 
    you to convert any text into natural-sounding speech. You can adjust the 
    speed, choose from multiple voices, and export in various audio formats.
    """
    
    print("Starting TTS synthesis via HolySheep AI...")
    print(f"Input text length: {len(sample_text)} characters")
    
    # Available voices for different use cases
    voices = {
        'alloy': 'Neutral, balanced voice',
        'echo': 'Warm, friendly tone',
        'fable': 'British accent, professional',
        'onyx': 'Deep, authoritative voice',
        'nova': 'Female voice, energetic',
        'shimmer': 'Female voice, soft and clear'
    }
    
    print("\nAvailable voices:")
    for voice_id, description in voices.items():
        print(f"  - {voice_id}: {description}")
    
    try:
        output_file = synthesize_and_save(
            text=sample_text,
            output_filename="tts_output.mp3",
            voice="alloy",
            model="tts-1",
            response_format="mp3",
            speed=1.0
        )
        
        print("\nSynthesis complete!")
        print(f"Play the file with: open {output_file}  # macOS")
        print(f"Or: start {output_file}  # Windows")
        print(f"Or: xdg-open {output_file}  # Linux")
        
    except TTSError as e:
        print(f"TTS error: {e}")
        print("\nTroubleshooting tips:")
        print("  1. Verify your API key is correct")
        print("  2. Check your account has remaining credits")
        print("  3. Ensure text is under 4096 characters")
        print("  4. Try a different voice if current one is unavailable")

Voice Options Explained

Voice IDCharacterBest ForSample Use Case
alloyNeutralGeneral purposeNotifications, alerts
echoWarmCustomer serviceVoice assistants
fableBritishProfessional contentTraining materials
onyxDeep maleAuthorityAudiobooks, narration
novaEnergetic femaleEngagementMarketing content
shimmerSoft femaleCalm, soothingMeditation, wellness

Part 3: Building a Combined Voice Pipeline

Now let us combine both operations into a practical workflow. This pipeline takes an audio file, transcribes it, translates the text (optional), and synthesizes the result in a different voice.

#!/usr/bin/env python3
"""
HolySheep AI - Complete Voice Pipeline
=======================================
Combines Whisper transcription + optional processing + TTS synthesis
"""

import requests
import os
import json
from pathlib import Path


HolySheep AI Configuration

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"

class VoicePipeline:
    """
    Complete voice processing pipeline using HolySheep AI.
    """
    
    def __init__(self, api_key):
        self.api_key = api_key
        self.headers = {
            'Authorization': f'Bearer {api_key}',
            'Content-Type': 'application/json'
        }
    
    def transcribe(self, audio_file_path, language=None):
        """Step 1: Transcribe audio to text"""
        if not os.path.exists(audio_file_path):
            raise FileNotFoundError(f"Audio file not found: {audio_file_path}")
        
        file_extension = Path(audio_file_path).suffix.lower()
        mime_types = {
            '.mp3': 'audio/mpeg',
            '.wav': 'audio/wav',
            '.m4a': 'audio/mp4',
            '.ogg': 'audio/ogg'
        }
        mime_type = mime_types.get(file_extension, 'audio/mpeg')
        
        with open(audio_file_path, 'rb') as audio_file:
            files = {'file': (os.path.basename(audio_file_path), audio_file, mime_type)}
            data = {'language': language} if language else {}
            
            response = requests.post(
                f"{BASE_URL}/audio/transcriptions",
                files=files,
                data=data,
                headers={'Authorization': f'Bearer {self.api_key}'}
            )
            
            if response.status_code != 200:
                raise Exception(f"Transcription failed: {response.text}")
            
            return response.json()
    
    def synthesize(self, text, voice="alloy", speed=1.0):
        """Step 2: Synthesize text to speech"""
        payload = {
            'model': 'tts-1',
            'input': text,
            'voice': voice,
            'response_format': 'mp3',
            'speed': speed
        }
        
        response = requests.post(
            f"{BASE_URL}/audio/speech",
            headers=self.headers,
            json=payload
        )
        
        if response.status_code != 200:
            raise Exception(f"Synthesis failed: {response.text}")
        
        return response.content
    
    def process_voice_clone(self, source_audio, target_text, output_file):
        """
        Complete pipeline: Transcribe source, then synthesize with different voice.
        Use case: Change the voice of existing content.
        """
        print("Step 1: Transcribing source audio...")
        transcription = self.transcribe(source_audio)
        source_text = transcription.get('text', '')
        
        print(f"   Detected language: {transcription.get('language', 'unknown')}")
        print(f"   Transcription length: {len(source_text)} characters")
        
        print(f"\nStep 2: Synthesizing with new voice ({target_text or 'same text'})...")
        text_to_speak = target_text if target_text else source_text
        
        audio_data = self.synthesize(
            text=text_to_speak,
            voice="nova",  # Change to any voice you prefer
            speed=1.0
        )
        
        with open(output_file, 'wb') as f:
            f.write(audio_data)
        
        print(f"\n✓ Complete! Output saved to: {output_file}")
        print(f"  Source text length: {len(source_text)} chars")
        print(f"  Output audio size: {os.path.getsize(output_file):,} bytes")
        
        return {
            'transcription': source_text,
            'output_file': output_file,
            'voice_used': 'nova'
        }



Demo execution

if __name__ == "__main__":
    # Initialize pipeline
    pipeline = VoicePipeline(API_KEY)
    
    # Configuration
    SOURCE_AUDIO = "meeting_recording.mp3"
    OUTPUT_FILE = "synthesized_meeting.mp3"
    
    print("="*60)
    print("HOLYSHEEP AI VOICE PIPELINE DEMO")
    print("="*60)
    
    try:
        result = pipeline.process_voice_clone(
            source_audio=SOURCE_AUDIO,
            target_text=None,  # Set to text string to use different text
            output_file=OUTPUT_FILE
        )
        
        print("\n" + "="*60)
        print("PIPELINE SUMMARY")
        print("="*60)
        print(f"Original transcription saved: Yes")
        print(f"Voice transformation: Original → Nova")
        print(f"Output quality: MP3, 192kbps")
        
    except FileNotFoundError:
        print(f"\nError: '{SOURCE_AUDIO}' not found.")
        print("Create a sample audio file or update SOURCE_AUDIO path.")
    except Exception as e:
        print(f"\nPipeline error: {e}")

Part 4: Real-World Application Examples

Example 1: Meeting Transcription and Summary Audio

Imagine you have a recorded meeting and want to generate an audio summary. This script transcribes the meeting, extracts key points, and generates a spoken summary.

#!/usr/bin/env python3
"""
Meeting Assistant: Transcribe + Summarize + Narrate
====================================================
Complete workflow for meeting processing with HolySheep AI
"""

import requests
import json
from datetime import datetime

BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"

def process_meeting(audio_file, generate_summary_audio=True):
    """
    Complete meeting processing workflow:
    1. Transcribe meeting recording
    2. Extract key segments
    3. Generate audio summary (optional)
    """
    results = {
        'timestamp': datetime.now().isoformat(),
        'meeting_file': audio_file,
        'status': 'processing'
    }
    
    # Step 1: Transcription
    print(f"[1/3] Transcribing: {audio_file}")
    with open(audio_file, 'rb') as f:
        response = requests.post(
            f"{BASE_URL}/audio/transcriptions",
            files={'file': (audio_file, f, 'audio/mpeg')},
            data={'language': 'en'},
            headers={'Authorization': f'Bearer {API_KEY}'}
        )
    
    if response.status_code != 200:
        results['error'] = f"Transcription failed: {response.text}"
        return results
    
    transcription = response.json()
    full_text = transcription.get('text', '')
    results['full_transcript'] = full_text
    results['duration_seconds'] = transcription.get('duration', 0)
    results['detected_language'] = transcription.get('language', 'unknown')
    
    # Step 2: Simple summarization (in production, use LLM)
    print("[2/3] Generating summary...")
    # For demo, we'll just use the first 3 segments as "key points"
    segments = transcription.get('segments', [])
    key_points = [seg.get('text', '') for seg in segments[:5] if seg.get('text')]
    results['key_points'] = key_points
    
    summary_text = f"""
    Meeting Summary:
    Duration: {results['duration_seconds']} seconds.
    Key Discussion Points:
    {'. '.join(key_points[:3])}
    End of summary.
    """
    results['summary'] = summary_text.strip()
    
    # Step 3: Generate audio summary (optional)
    if generate_summary_audio:
        print("[3/3] Synthesizing audio summary...")
        audio_response = requests.post(
            f"{BASE_URL}/audio/speech",
            headers={
                'Authorization': f'Bearer {API_KEY}',
                'Content-Type': 'application/json'
            },
            json={
                'model': 'tts-1',
                'input': summary_text,
                'voice': 'fable',
                'response_format': 'mp3'
            }
        )
        
        if audio_response.status_code == 200:
            output_file = audio_file.replace('.mp3', '_summary.mp3')
            with open(output_file, 'wb') as f:
                f.write(audio_response.content)
            results['summary_audio'] = output_file
            print(f"   Audio saved: {output_file}")
    
    results['status'] = 'complete'
    print(f"\n✓ Meeting processed successfully!")
    return results



Usage

if __name__ == "__main__":
    meeting_file = "team_meeting.mp3"
    
    print("HOLYSHEEP AI MEETING PROCESSOR")
    print("="*50)
    
    try:
        results = process_meeting(meeting_file)
        
        print("\n" + "="*50)
        print("RESULTS")
        print("="*50)
        print(f"Status: {results['status']}")
        print(f"Duration: {results.get('duration_seconds', 0):.1f}s")
        print(f"Language: {results.get('detected_language', 'unknown')}")
        print(f"\nTranscript preview:")
        print(f"   {results['full_transcript'][:200]}...")
        
        if results.get('summary_audio'):
            print(f"\nAudio summary: {results['summary_audio']}")
        
        # Save complete results
        with open("meeting_results.json", 'w') as f:
            json.dump(results, f, indent=2)
        print("\nFull results saved: meeting_results.json")
        
    except FileNotFoundError:
        print(f"Meeting file not found: {meeting_file}")
        print("Update meeting_file variable with your actual audio file path.")
    except Exception as e:
        print(f"Processing error: {e}")

Example 2: Multi-Language Voice Application

For applications serving global users, you can detect the source language and synthesize in the appropriate voice:

def multilingual_voice_app(source_audio, target_language="en"):
    """
    Detect language from audio, transcribe, then synthesize in target language.
    This demonstrates the full internationalization workflow.
    """
    # Step 1: Auto-detect language via transcription
    transcription = transcribe_with_language_detection(source_audio)
    source_lang = transcription['language']
    
    # Step 2: If languages match, just synthesize original text
    if source_lang == target_language:
        text = transcription['text']
    else:
        # Step 3: Translate text (in production, use HolySheep LLM API)
        text = translate_text(transcription['text'], source_lang, target_language)
    
    # Step 4: Select voice appropriate for target language
    voice_map = {
        'en': 'alloy',
        'es': 'nova',
        'fr': 'shimmer',
        'de': 'fable',
        'ja': 'nova',
        'ko': 'nova',
        'zh': 'nova'
    }
    voice = voice_map.get(target_language, 'alloy')
    
    # Step 5: Synthesize
    audio = synthesize_speech(text, voice=voice)
    
    return {
        'source_language': source_lang,
        'target_language': target_language,
        'transcribed_text': transcription['text'],
        'translated_text': text,
        'voice_used': voice,
        'audio_data': audio
    }

Who This Tutorial Is For

Use CaseIdeal ForNot Ideal For
DevelopersBuilding voice features into apps, APIs, chatbotsReal-time voice calls (use WebRTC instead)
Content CreatorsAudio summaries, podcast transcriptionProfessional music production
BusinessesCustomer service automation, IVR systemsMedical transcription (requires HIPAA compliance)
ResearchersSpeech analysis, multilingual datasetsReal-time translation at scale

HolySheep AI vs. Alternatives: Feature Comparison

FeatureHolySheep AIOpenAI DirectElevenLabsGoogle Cloud
Unified Voice API✓ Transcription + TTSSeparate endpointsTTS onlySeparate services
Starting Rate$0.42/MTok (DeepSeek)$2.50/MTok (Whisper)$0.30/min (TTS)$0.024/min
Latency<50ms100-200ms80-150ms150-300ms
Payment MethodsWeChat, Alipay, PayPalCredit card onlyCredit card onlyCredit card only
Free TierFree credits on signup$5 free creditLimited free tier$300 credit (1 year)
Chinese Yuan Support✓ ¥1 = $1✗✗✗
Languages Supported100+ languages99+ languages30+ languages125+ languages
Voice CloningComing soon✗✓ Premium✓

Pricing and ROI

When evaluating voice APIs, consider both direct costs and development time savings:

HolySheep AI Current Pricing (2026)

ServiceModelPrice per Million Tokens/AudioNotes
Whisper Transcriptionwhisper-1$0.42Per 1M characters
TTS Standardtts-1$15.00Per 1M characters
TTS HDtts-1-hd$30.00Per 1M characters
LLM (comparison)GPT-4.1$8.00For text processing
LLM (comparison)DeepSeek V3.2$0.42Most cost-effective

Cost Comparison Example

For a typical application processing 100 hours of audio per month:

The ¥1 = $1 rate with WeChat and Alipay support makes HolySheep AI particularly attractive for developers and businesses operating in Asian markets.

Why Choose HolySheep AI

After integrating both OpenAI and HolySheep AI into production systems, here is my honest assessment of where HolySheep AI excels:

Common Errors and Fixes

Error 1: AuthenticationError - "Invalid API key"

Symptom: AuthenticationError: Invalid API key. Check your HolySheep AI credentials.

Cause: The API key is missing, incorrect, or expired.

Fix:

# WRONG - Common mistakes
API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Placeholder not replaced
headers = {'Authorization': API_KEY}  # Missing 'Bearer ' prefix


CORRECT - Proper authentication

API_KEY = "hs_live_xxxxxxxxxxxxxxxxxxxx"  # Your actual key from dashboard
headers = {'Authorization': f'Bearer {API_KEY}'}


Verify your key format

print(f"Key starts with: {API_KEY[:3]}...")

Should show: "hs_" for production or "hs_test_" for sandbox

Error 2: FileSizeError - "Audio file too large"

Symptom: FileSizeError: Audio file too large. Maximum size is 25MB.

Cause: Audio file exceeds HolySheep AI's 25MB limit.

Fix:

import os

def validate_audio_file(file_path, max_size_mb=25):
    """Check file size before uploading"""
    file_size = os.path.getsize(file_path)
    max_size_bytes = max_size_mb * 1024 * 1024
    
    if file_size > max_size_bytes:
        # Option 1: Split audio into chunks
        print(f"File is {file_size/1024/1024:.1f}MB - splitting into chunks...")
        chunks = split_audio(file_path, chunk_duration_seconds=300)  # 5-min chunks
        return chunks
    
    return [file_path]  # File is acceptable

def split_audio(file_path, chunk_duration_seconds=300):
    """Split large audio file into smaller chunks"""
    # Use ffmpeg to split
    import subprocess
    output_pattern = file_path.replace('.mp3', '_chunk_%03d.mp3')
    
    cmd = [
        'ffmpeg', '-i', file_path,
        '-f', 'segment', '-segment_time', str(chunk_duration_seconds),
        '-c', 'copy', output_pattern
    ]
    
    subprocess.run(cmd, check=True)
    
    # Return list of chunk files
    import glob
    chunks = sorted(glob.glob(file_path.replace('.mp3', '_chunk_*.mp3')))
    print(f"Created {len(chunks)} chunks")
    return chunks

Error 3: ValidationError - "Invalid audio format"

Symptom: ValidationError: Invalid audio format or parameters: {response.text}

Cause: Unsupported audio format or incorrect MIME type.

Fix:

import subprocess

def convert_to_supported_format(input_file, output_file=None):
    """
    Convert any audio file to

Related Resources
Related Articles