Migration guide: Google Speech-to-Text to AssemblyAI

Documentation Index

Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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Get Started

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Side-by-side code comparison

from google.cloud import speech

client = speech.SpeechClient()

audio = speech.RecognitionAudio(
    uri="gs://cloud-samples-tests/speech/Google_Gnome.wav"
)

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code="en-US",
    model="video", # Chosen model
)

operation = client.long_running_recognize(config=config, audio=audio)

print("Waiting for operation to complete...")
response = operation.result(timeout=90)

for i, result in enumerate(response.results):
    alternative = result.alternatives[0]
    print("-" * 20)
    print(f"First alternative of result {i}")
    print(f"Transcript: {alternative.transcript}")

import assemblyai as aai

aai.settings.api_key = "YOUR-API-KEY"
transcriber = aai.Transcriber()

# You can use a local filepath:
# audio_file = "./example.mp3"
# Or use a publicly-accessible URL:
audio_file = (
    "https://assembly.ai/sports_injuries.mp3"
)

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
)
transcript = transcriber.transcribe(audio_file, config)

if transcript.status == aai.TranscriptStatus.error:
    print(f"Transcription failed: {transcript.error}")
    exit(1)

print(transcript.text)

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Installation

from google.cloud import speech

client = speech.SpeechClient()

import assemblyai as aai

aai.settings.api_key = "YOUR-API-KEY"
transcriber = aai.Transcriber()

• Store your API key securely in an environment variable
• API key authentication works the same across all AssemblyAI SDKs

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Audio File Sources

audio = speech.RecognitionAudio(uri="gs://cloud-samples-tests/speech/Google_Gnome.wav")

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    language_code="en-US",
    model="video", # Chosen model
)

operation = client.long_running_recognize(config=config, audio=audio)

transcriber = aai.Transcriber()

# Local files
transcript = transcriber.transcribe("./audio.mp3")

# Public URLs
transcript = transcriber.transcribe("https://example.com/audio.mp3")

# S3 files (using pre-signed URLs)
s3_client = boto3.client('s3')
presigned_url = s3_client.generate_presigned_url(
    'get_object',
    Params={'Bucket': 'my-bucket', 'Key': 'audio.mp3'},
    ExpiresIn=3600
)
transcript = transcriber.transcribe(presigned_url)

• There’s no need to specify the audio encoding format when using AssemblyAI - we have a transcoding pipeline under the hood which works on all supported file types so that you can get the most accurate transcription.
• You can submit a local file, URL, stream, buffer, blob, etc., directly to our transcriber. Check out some common ways you can host audio files here.
• You can transcribe audio files that are up to 10 hours long and you can transcribe multiple files in parallel. The default amount of jobs you can transcribe at once is 200 while on the PAYG plan.

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Basic Transcription

print("Waiting for operation to complete...")
response = operation.result(timeout=90)

for i, result in enumerate(response.results):
    alternative = result.alternatives[0]
    print("-" * 20)
    print(f"First alternative of result {i}")
    print(f"Transcript: {alternative.transcript}")

transcriber = aai.Transcriber()

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
)
transcript = transcriber.transcribe(audio_file, config)

if transcript.status == aai.TranscriptStatus.error:
    print(f"Transcription failed: {transcript.error}")
else:
    print(transcript.text)

• The SDK handles polling under the hood.
• The full transcript is directly accessible via transcript.text.
• English is the default language. We recommend specifying speech_models=["universal-3-pro", "universal-2"] for the highest accuracy.
• We have a cookbook for error handling common errors when using our API.

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Adding Features

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=8000,
    language_code="en-US",
    enable_speaker_diarization=True,  # Speaker diarization
    diarization_speaker_count=2,  # Specify amount of speakers
    profanity_filter=True   # Remove profanity from transcript
)

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    speaker_labels=True,  # Speaker diarization
    filter_profanity=True,  # Remove profanity from transcript
    speakers_expected=2,  # Specify amount of speakers in audio
)

transcript = transcriber.transcribe(audio_file, config)

# Access speaker labels

for utterance in transcript.utterances:
    print(f"Speaker {utterance.speaker}: {utterance.text}")

• Use aai.TranscriptionConfig to specify any extra features that you wish to use.
• The results for Speaker Diarization are stored in transcript.utterances. To see the full transcript response object, refer to our API Reference.
• Check our documentation for our full list of available features and their parameters.