오디오 이해

ai.google.dev에서 보기 Google Colab에서 실행 Kaggle에서 실행 Vertex AI에서 열기 GitHub에서 소스 보기

Gemma 3n부터 오디오를 프롬프트와 워크플로에 직접 사용할 수 있습니다. 오디오와 음성 언어는 사용자 의도를 파악하고, 주변 세계에 관한 정보를 기록하고, 해결해야 할 특정 문제를 이해하는 데 유용한 데이터 소스입니다.

이 가이드에서는 자동 음성 인식 (ASR), 번역, 일반적인 음성 이해를 비롯한 Gemma 4의 오디오 처리 기능을 간략하게 설명합니다.

이 노트북은 T4 GPU에서 실행됩니다.

Python 패키지 설치

Gemma 모델을 실행하고 요청을 보내는 데 필요한 Hugging Face 라이브러리를 설치합니다.

# Install PyTorch & other libraries
pip install torch accelerate

# Install the transformers library
pip install transformers

모델 로드

다음 코드 예와 같이 transformers 라이브러리를 사용하여 AutoProcessor 및 AutoModelForImageTextToText 클래스를 사용하여 processor 및 model의 인스턴스를 만듭니다.

MODEL_ID = "google/gemma-4-E2B-it" # @param ["google/gemma-4-E2B-it","google/gemma-4-E4B-it", "google/gemma-4-31B-it", "google/gemma-4-26B-A4B-it"]

from transformers import AutoProcessor, AutoModelForMultimodalLM

model = AutoModelForMultimodalLM.from_pretrained(MODEL_ID, dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)

Loading weights:   0%|          | 0/2011 [00:00<?, ?it/s]

오디오 데이터

디지털 오디오 데이터는 다양한 형식과 해상도로 제공될 수 있습니다. MP3 및 WAV 형식과 같이 Gemma에서 사용할 수 있는 실제 오디오 형식은 사운드 데이터를 텐서로 변환하는 데 선택한 프레임워크에 따라 결정됩니다. 다음은 Gemma로 처리하기 위해 오디오 데이터를 준비할 때 고려해야 할 몇 가지 구체적인 사항입니다.

  • 토큰 비용: Gemma 4의 경우 오디오 1초당 토큰 25개입니다. (Gemma 3n의 경우 토큰 6.25개)
  • 클립 길이: 오디오는 최대 30초의 길이를 지원합니다.
  • 오디오 채널: 오디오 데이터는 단일 오디오 채널로 처리됩니다. 왼쪽 및 오른쪽 채널과 같은 다중 채널 오디오를 사용하는 경우 채널을 삭제하거나 사운드 데이터를 단일 채널로 결합하여 데이터를 단일 채널로 줄이는 것이 좋습니다.
  • 기술 인코딩:
    • 샘플링 레이트: 32ms 프레임을 사용하는 16kHz
    • 비트 심도: 32비트 부동 소수점 형식으로, 샘플은 [-1, 1] 범위 내에서 정규화됩니다.

처리하려는 오디오 데이터가 특히 채널, 샘플링 레이트, 비트 심도 측면에서 입력 처리와 크게 다른 경우 모델에서 처리하는 데이터 해상도와 일치하도록 오디오 데이터를 다시 샘플링하거나 트리밍하는 것이 좋습니다.

오디오 인코딩

Hugging Face AutoProcessor와 같은 고급 라이브러리는 오디오 전처리를 자동으로 처리하는 경우가 많지만 때로는 커스텀 인코딩을 구현해야 할 수도 있습니다.

Gemma에서 사용할 자체 코드 구현으로 오디오 데이터를 인코딩할 때는 권장되는 변환 프로세스를 따라야 합니다. MP3 또는 WAV 인코딩 데이터와 같은 특정 형식으로 인코딩된 오디오 파일을 사용하는 경우 먼저 ffmpeg와 같은 라이브러리를 사용하여 샘플로 디코딩해야 합니다. 데이터가 디코딩되면 오디오를 [-1, 1] 범위의 모노 채널, 16kHz float32 파형으로 변환합니다. 예를 들어 44.1kHz에서 스테레오 서명된 16비트 PCM 정수 WAV 파일을 사용하는 경우 다음 단계를 따르세요.

  • 오디오 데이터를 16kHz로 다시 샘플링합니다.
  • 2개 채널의 평균을 구하여 스테레오에서 모노로 다운믹스합니다.
  • int16에서 float32로 변환하고 32768.0으로 나누어 [-1, 1] 범위로 조정합니다.

음성 텍스트 변환

Gemma 4 E2B 및 E4B는 다국어 음성 인식을 위해 학습되므로 다양한 언어로 오디오 입력을 텍스트로 변환할 수 있습니다. 다음 코드 예에서는 Hugging Face Transformers를 사용하여 오디오 파일에서 텍스트를 변환하도록 모델에 프롬프트를 표시하는 방법을 보여줍니다.

RESOURCE_URL_PREFIX = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/"

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."},
            #{"type": "text", "text": "Transcribe the following speech segment in English into English text. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal1.wav"},
        ]
    }
]

input_ids = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True, return_dict=True,
        return_tensors="pt",
)
input_ids = input_ids.to(model.device, dtype=model.dtype)

outputs = model.generate(**input_ids, max_new_tokens=64)

text = processor.batch_decode(
    outputs,
    skip_special_tokens=False,
    clean_up_tokenization_spaces=False
)
print(text[0])

<bos><|turn>user
Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:

* Only output the transcription, with no newlines.
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<|turn>model
I woke up early today feeling really fresh the morning light was beautiful and I enjoyed a nice cup of coffee<turn|>

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Give me a concise overview of these audio files."},
            {"type": "text", "text": "journal1:"},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal1.wav"},
            {"type": "text", "text": "journal2:"},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal2.wav"},
            {"type": "text", "text": "journal3:"},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal3.wav"},
            {"type": "text", "text": "journal4:"},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal4.wav"},
            {"type": "text", "text": "journal5:"},
            {"type": "audio", "audio": f"{RESOURCE_URL_PREFIX}journal5.wav"},
        ]
    }
]

input_ids = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True, return_dict=True,
        return_tensors="pt",
)
input_ids = input_ids.to(model.device, dtype=model.dtype)

outputs = model.generate(**input_ids, max_new_tokens=1024)

text = processor.batch_decode(
    outputs,
    skip_special_tokens=False,
    clean_up_tokenization_spaces=False
)
print(text[0])

<bos><|turn>user
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<|turn>model
Here is a concise overview of the audio files:

**Journal 1:** The speaker felt refreshed, enjoyed a morning ride, a cup of coffee, and was generally happy.

**Journal 2:** The speaker spent the afternoon at the park, which was a perfect day for a walk, and enjoyed watching the cherry blossoms.

**Journal 3:** The speaker finished the day with a good book, feeling grateful for simple moments and ready for more.

**Journal 4:** The speaker returned from work, admiring the sunset, and enjoyed a clear view from the train.

**Journal 5:** The speaker had a great lunch with an old friend, enjoyed catching up, and felt happy about the day.<turn|>

자동 음성 번역

Gemma 4 E2B 및 E4B는 다국어 음성 번역 작업을 위해 학습되므로 음성 오디오를 다른 언어로 직접 번역할 수 있습니다. 다음 코드 예에서는 Hugging Face Transformers를 사용하여 음성 오디오를 텍스트로 번역하도록 모델에 프롬프트를 표시하는 방법을 보여줍니다.

messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean."},
            {"type": "audio", "audio": "https://ai.google.dev/gemma/docs/audio/roses-are.wav"},
        ]
    }
]

input_ids = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True, return_dict=True,
        return_tensors="pt",
)
input_ids = input_ids.to(model.device, dtype=model.dtype)

outputs = model.generate(**input_ids, max_new_tokens=64)

text = processor.batch_decode(
    outputs,
    skip_special_tokens=False,
    clean_up_tokenization_spaces=False
)
print(text[0])

<bos><|turn>user
Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean.<|audio><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><audio|><turn|>
<|turn>model
Roses are red, violets are blue.
Korean: 장미는 빨갛고, 제비꽃은 파랗다.<turn|>

자동 음성 번역 / 자동 음성 인식

직접 사용해 보기

pip install ipywebrtc

원 버튼을 누르고 말하기 시작합니다. 완료되면 원 버튼을 다시 클릭합니다. 위젯이 캡처한 내용을 즉시 재생하기 시작합니다.

from google.colab import output
output.enable_custom_widget_manager()

from ipywebrtc import AudioRecorder, CameraStream

camera = CameraStream(constraints={'audio': True,'video':False})
recorder = AudioRecorder(stream=camera)
recorder

AudioRecorder(audio=Audio(value=b'', format='webm'), stream=CameraStream(constraints={'audio': True, 'video': …

PyTorch에서 이해할 수 있는 wav 형식으로 webm 파일을 변환합니다.

with open('/content/recording.webm', 'wb') as f:
    f.write(recorder.audio.value)
!ffmpeg -i /content/recording.webm /content/recording.wav -y

ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers
  built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)
  configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 70.100 / 56. 70.100
  libavcodec     58.134.100 / 58.134.100
  libavformat    58. 76.100 / 58. 76.100
  libavdevice    58. 13.100 / 58. 13.100
  libavfilter     7.110.100 /  7.110.100
  libswscale      5.  9.100 /  5.  9.100
  libswresample   3.  9.100 /  3.  9.100
  libpostproc    55.  9.100 / 55.  9.100
Input #0, matroska,webm, from '/content/recording.webm':
  Metadata:
    encoder         : Chrome
  Duration: 00:00:04.02, start: 0.000000, bitrate: 131 kb/s
  Stream #0:0(eng): Audio: opus, 48000 Hz, mono, fltp (default)
Stream mapping:
  Stream #0:0 -> #0:0 (opus (native) -> pcm_s16le (native))
Press [q] to stop, [?] for help
Output #0, wav, to '/content/recording.wav':
  Metadata:
    ISFT            : Lavf58.76.100
  Stream #0:0(eng): Audio: pcm_s16le ([1][0][0][0] / 0x0001), 48000 Hz, mono, s16, 768 kb/s (default)
    Metadata:
      encoder         : Lavc58.134.100 pcm_s16le
size=     383kB time=00:00:04.01 bitrate= 779.7kbits/s speed=60.6x    
video:0kB audio:382kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.019914%

ASR

messages = [{
  "role": "user",
  "content": [
    {"type": "text", "text": "Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:\n* Only output the transcription, with no newlines.\n* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three."},
    {"type": "audio", "audio": "/content/recording.wav"},
  ]
}]

input_ids = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True, return_dict=True,
        return_tensors="pt",
)
input_ids = input_ids.to(model.device, dtype=model.dtype)

outputs = model.generate(**input_ids, max_new_tokens=64)

text = processor.batch_decode(
    outputs,
    skip_special_tokens=False,
    clean_up_tokenization_spaces=False
)
print(text[0])

<bos><|turn>user
Transcribe the following speech segment in its original language. Follow these specific instructions for formatting the answer:

* Only output the transcription, with no newlines.
* When transcribing numbers, write the digits, i.e. write 1.7 and not one point seven, and write 3 instead of three.<|audio><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><audio|><turn|>
<|turn>model
How can I get to the station?<turn|>

AST

messages = [{
  "role": "user",
  "content": [
    {"type": "text", "text": "Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean."},
    {"type": "audio", "audio": "/content/recording.wav"},
  ]
}]

input_ids = processor.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True, return_dict=True,
        return_tensors="pt",
)
input_ids = input_ids.to(model.device, dtype=model.dtype)

outputs = model.generate(**input_ids, max_new_tokens=64)

text = processor.batch_decode(
    outputs,
    skip_special_tokens=False,
    clean_up_tokenization_spaces=False
)
print(text[0])

<bos><|turn>user
Transcribe the following speech segment in English, then translate it into Korean. When formatting the answer, first output the transcription in English, then one newline, then output the string 'Korean: ', then the translation in Korean.<|audio><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><audio|><turn|>
<|turn>model
How can I get to the station?
Korean: 역에 어떻게 가나요?<turn|>

요약 및 다음 단계

이 가이드에서는 Gemma 4 모델을 사용하여 오디오를 처리하는 방법을 알아보았습니다. 이 예에서는 음성 언어를 변환하는 음성 텍스트 변환 (ASR)과 음성 오디오를 다른 언어로 직접 번역하는 자동 음성 번역 (AST)을 실행하는 방법을 보여주었습니다. 또한 처리할 노트북 환경에서 마이크로 오디오를 캡처하는 방법도 알아보았습니다.

자세한 내용은 다음 문서를 참고하세요.