Audioverständnis

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Ab Gemma 3n können Sie Audio direkt in Ihre Prompts und Workflows einbinden. Audio und gesprochene Sprache sind reichhaltige Datenquellen, um Nutzerabsichten zu erfassen, Informationen über die Welt um uns herum aufzuzeichnen und spezifische Probleme zu verstehen, die gelöst werden müssen.

In diesem Leitfaden finden Sie eine Übersicht über die Audioverarbeitungsfunktionen von Gemma 4, einschließlich automatischer Spracherkennung (ASR), Übersetzung und allgemeiner Spracherkennung.

Dieses Notebook wird auf einer T4-GPU ausgeführt.

Python-Pakete installieren

Installieren Sie die Hugging Face-Bibliotheken, die zum Ausführen des Gemma-Modells und zum Senden von Anfragen erforderlich sind.

# Install PyTorch & other libraries
pip install torch accelerate

# Install the transformers library
pip install transformers

Modell laden

Verwenden Sie die transformers-Bibliotheken, um eine Instanz von processor und model mit den Klassen AutoProcessor und AutoModelForImageTextToText zu erstellen, wie im folgenden Codebeispiel gezeigt:

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]

Audiodaten

Digitale Audiodaten können in vielen Formaten und Auflösungen vorliegen. Die tatsächlichen Audioformate, die Sie mit Gemma verwenden können, z. B. MP3- und WAV-Formate, werden durch das Framework bestimmt, das Sie zum Konvertieren von Tondaten in Tensoren auswählen. Hier sind einige spezifische Überlegungen zur Vorbereitung von Audiodaten für die Verarbeitung mit Gemma:

  • Token-Kosten:Jede Sekunde Audio entspricht 25 Tokens für Gemma 4. (6,25 Tokens für Gemma 3n)
  • Cliplänge:Audio kann maximal 30 Sekunden lang sein.
  • Audio-Channels:Audiodaten werden als einzelner Audio-Channel verarbeitet. Wenn Sie Mehrkanal-Audio verwenden, z. B. den linken und rechten Kanal, sollten Sie die Daten auf einen einzelnen Kanal reduzieren, indem Sie Kanäle entfernen oder die Tondaten in einem einzelnen Kanal zusammenfassen.
  • Technische Codierung:
    • Abtastrate:16 kHz mit 32‑ms-Frames.
    • Bittiefe:32-Bit-Gleitkommaformat, wobei die Samples im Bereich [–1, 1] normalisiert werden.

Wenn sich die Audio-Daten, die Sie verarbeiten möchten, erheblich von der Eingabeverarbeitung unterscheiden, insbesondere in Bezug auf Kanäle, Abtastrate und Bit-Tiefe, sollten Sie Ihre Audio-Daten neu abtasten oder zuschneiden, damit sie der Datenauflösung des Modells entsprechen.

Audiocodierung

Während Bibliotheken auf hoher Ebene (z. B. Hugging Face AutoProcessor) die Audio-Vorverarbeitung oft automatisch übernehmen, müssen Sie manchmal eine benutzerdefinierte Codierung implementieren.

Wenn Sie Audiodaten mit Ihrer eigenen Code-Implementierung für die Verwendung mit Gemma codieren, sollten Sie den empfohlenen Konvertierungsprozess befolgen. Wenn Sie mit Audiodateien arbeiten, die in einem bestimmten Format codiert sind, z. B. MP3- oder WAV-codierte Daten, müssen Sie diese zuerst mit einer Bibliothek wie ffmpeg in Samples decodieren. Nachdem die Daten decodiert wurden, konvertieren Sie das Audio in Mono-Channel-Wellenformen im Bereich [–1, 1] mit einer float32-Abtastrate von 16 kHz. Wenn Sie beispielsweise mit Stereo-WAV-Dateien mit vorzeichenbehafteten 16-Bit-PCM-Ganzzahlen bei 44, 1 kHz arbeiten, gehen Sie so vor:

  • Audiodaten auf 16 kHz resamplen
  • Stereo- zu Mono-Downmix durch Mittelung der beiden Kanäle
  • Von int16 in float32 konvertieren und durch 32768,0 dividieren, um auf den Bereich [–1, 1] zu skalieren.

Spracherkennung

Gemma 4 E2B und E4B sind für die mehrsprachige Spracherkennung trainiert. Sie können Audioeingaben in verschiedenen Sprachen in Text transkribieren. Die folgenden Codebeispiele zeigen, wie Sie das Modell auffordern, Text aus Audiodateien mit Hugging Face Transformers zu transkribieren:

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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dio|><|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|><|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
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|>

Automatisierte Sprachübersetzung

Gemma 4 E2B und E4B wurden für mehrsprachige Sprachübersetzungsaufgaben trainiert. So können Sie gesprochene Audioinhalte direkt in eine andere Sprache übersetzen. Die folgenden Codebeispiele zeigen, wie Sie das Modell auffordern, gesprochene Audioinhalte mit Hugging Face Transformers in Text zu übersetzen:

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|>

Automatische Sprachübersetzung / Automatische Spracherkennung

Selbst ausprobieren

pip install ipywebrtc

Drücke die Kreistaste und beginne zu sprechen. Klicken Sie noch einmal auf die Kreistaste, wenn Sie fertig sind. Das Widget beginnt sofort mit der Wiedergabe der aufgenommenen Inhalte.

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': …

Konvertieren Sie die WEBM-Datei in das WAV-Format, das von PyTorch verarbeitet werden kann.

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%

Erweiterte Suchberichte

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|>

Zusammenfassung und nächste Schritte

In dieser Anleitung haben Sie gelernt, wie Sie Audio mit Gemma 4-Modellen verarbeiten. In den Beispielen wurde gezeigt, wie mit Speech-to-Text (ASR) gesprochene Sprache transkribiert und mit Automated Speech Translation (AST) gesprochene Audioinhalte direkt in eine andere Sprache übersetzt werden. Sie haben auch gesehen, wie Sie Audio von einem Mikrofon in einer Notebook-Umgebung zur Verarbeitung aufnehmen.

Weitere Informationen finden Sie in der folgenden Dokumentation.