Transcribe audio

Speech-to-Text enables easy integration of Google speech recognition technologies into your solution. Speech-to-Text is a machine learning (ML) technology that gives you full control over your infrastructure and your protected speech data to meet data residency and compliance requirements.

The following table describes the key capabilities of Speech-to-Text.

Supported audio encodings for Speech-to-Text

The Speech-to-Text API supports a number of different encodings. The following table lists supported audio codecs:

FLAC is both an audio codec and an audio file format. To transcribe audio files using FLAC encoding, you must provide them in the .FLAC file format, which includes a header containing metadata.

Speech-to-Text supports WAV files with LINEAR16 or MULAW encoded audio.

For more information on Speech-to-Text audio codecs, consult the AudioEncoding reference documentation.

If you have a choice when encoding the source material, use a lossless encoding such as FLAC or LINEAR16 for better speech recognition.

Before you begin

To get the permissions you need to use the Vertex AI Speech-to-Text pre-trained API, ask your Project IAM Admin to grant you the AI Speech Developer (ai-speech-developer) role in your project namespace.

How to use the Speech-to-Text client library

Work through the following steps to use the Speech-to-Text client library using Python:

# Import the Speech-to-Text client library.
from google.cloud import speech

# Instantiate a client.
client = speech.SpeechClient()

# Specify the audio file to transcribe.
audio_uri = "YOUR_AUDIO_TO_TRANSCRIBE"

audio = speech.RecognitionAudio(uri=audio_uri)

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=16000,
    audio_channel_count=1,
    language_code="LANGUAGE_CODE",
)

metadata = [("x-goog-user-project", "projects/PROJECT_ID")]

# Detect speech in the audio file.
response = client.recognize(config=config, audio=audio, metadata=metadata)

for result in response.results:
    print("Transcript: {}".format(result.alternatives[0].transcript))

Sample of the Speech-to-Text client library

To transcribe an audio file using the Speech-to-Text API, first view the statuses and endpoints of the pre-trained models to identify your endpoint. Then, follow the sample code:

from google.cloud import speech_v1p1beta1
from google.cloud.speech_v1p1beta1.services.speech import client
from google.api_core.client_options import ClientOptions
import grpc
import io

def transcribe(local_file_path, api_endpoint):

  opts = ClientOptions(api_endpoint=api_endpoint)
  tc = client.SpeechClient(credentials=creds, client_options=opts)
  config = {
    "encoding": speech_v1p1beta1.RecognitionConfig.AudioEncoding.LINEAR16,
    "language_code": "LANGUAGE_CODE",
    "sample_rate_hertz": 16000,
    "audio_channel_count": 1
  }

  metadata = (("x-goog-user-project", "projects/PROJECT_ID"),)

  with io.open(local_file_path, "rb") as f:
    content = f.read()
    audio = {"content": content}
    response = tc.recognize(request={"config": config, "audio": audio}, metadata=metadata)

Replace LANGUAGE_CODE with a supported language code.

Supported languages

The following languages are supported by Speech-to-Text: