 Lihat di ai.google.dev
|
 Jalankan di Google Colab
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 Jalankan di Kaggle
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 Buka di Vertex AI
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 Lihat sumber di GitHub
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Mulai dari Gemma 3n, Anda dapat menggunakan audio langsung ke dalam perintah dan alur kerja Anda. Audio dan bahasa lisan adalah sumber data yang kaya untuk menangkap maksud pengguna, merekam informasi tentang dunia di sekitar kita, dan memahami masalah spesifik yang harus dipecahkan.
Panduan ini memberikan ringkasan kemampuan pemrosesan audio Gemma 4, termasuk pengenalan ucapan otomatis (ASR), terjemahan, dan pemahaman ucapan umum.
Notebook ini akan berjalan di GPU T4.
Menginstal paket Python
Instal library Hugging Face yang diperlukan untuk menjalankan model Gemma dan membuat permintaan.
# Install PyTorch & other librariespip install torch accelerate# Install the transformers librarypip install transformers
Memuat Model
Gunakan library transformers untuk membuat instance processor dan model menggunakan class AutoProcessor dan AutoModelForImageTextToText seperti yang ditunjukkan dalam contoh kode berikut:
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]
Data audio
Data audio digital dapat memiliki banyak format dan tingkat resolusi. Format audio sebenarnya yang dapat Anda gunakan dengan Gemma, seperti format MP3 dan WAV, ditentukan oleh framework yang Anda pilih untuk mengonversi data suara menjadi tensor. Berikut beberapa pertimbangan khusus untuk menyiapkan data audio agar dapat diproses dengan Gemma:
- Biaya token: Setiap detik audio adalah 25 token untuk Gemma 4. (6,25 token untuk Gemma 3n).
- Durasi klip: Audio mendukung durasi maksimum 30 detik.
- Saluran audio: Data audio diproses sebagai satu saluran audio. Jika Anda menggunakan audio multi-saluran, seperti saluran kiri dan kanan, pertimbangkan untuk mengurangi data menjadi satu saluran dengan menghapus saluran atau menggabungkan data suara menjadi satu saluran.
- Encoding Teknis:
- Frekuensi Sampel: 16 kHz menggunakan frame 32 md.
- Kedalaman Bit: Format float 32-bit, dengan sampel yang dinormalisasi dalam rentang [-1, 1].
Jika data audio yang akan Anda proses sangat berbeda dari pemrosesan input, terutama dalam hal saluran, frekuensi sampel, dan kedalaman bit, pertimbangkan untuk melakukan resampling atau memangkas data audio agar sesuai dengan resolusi data yang ditangani oleh model.
Encoding audio
Meskipun library tingkat tinggi (seperti Hugging Face AutoProcessor) sering kali menangani praproses audio secara otomatis, terkadang Anda perlu menerapkan encoding kustom.
Saat mengenkode data audio dengan penerapan kode Anda sendiri untuk digunakan dengan Gemma, Anda harus mengikuti proses konversi yang direkomendasikan. Jika Anda bekerja
dengan file audio yang dienkode dalam format tertentu, seperti data yang dienkode MP3 atau WAV,
Anda harus mendekodenya terlebih dahulu menjadi sampel menggunakan library seperti ffmpeg. Setelah
data didekode, konversikan audio menjadi bentuk gelombang float32 16 kHz satu saluran
dalam rentang [-1, 1]. Misalnya, jika Anda menggunakan file WAV bilangan bulat PCM 16-bit bertanda tangan stereo pada 44,1 kHz, ikuti langkah-langkah berikut:
- Mengambil ulang sampel data audio ke 16 kHz
- Downmix dari stereo ke mono dengan merata-ratakan 2 saluran
- Konversi dari int16 ke float32, dan bagi dengan 32768.0 untuk menskalakan ke rentang [-1, 1]
Speech-to-text
Gemma 4 E2B dan E4B dilatih untuk pengenalan ucapan multibahasa, sehingga Anda dapat mentranskripsikan input audio dalam berbagai bahasa menjadi teks. Contoh kode berikut menunjukkan cara meminta model untuk mentranskripsikan teks dari file audio menggunakan Hugging Face Transformers:
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. * 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|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|audio|><|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 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 Give me a concise overview of these audio 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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|>
Terjemahan ucapan otomatis
Gemma 4 E2B dan E4B dilatih untuk tugas terjemahan ucapan multibahasa, sehingga Anda dapat menerjemahkan audio lisan langsung ke bahasa lain. Contoh kode berikut menunjukkan cara meminta model untuk menerjemahkan audio lisan ke dalam teks menggunakan Hugging Face Transformers:
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|>
Terjemahan Ucapan Otomatis / Pengenalan Ucapan Otomatis
Coba lakukan ini sendiri
pip install ipywebrtcTekan tombol lingkaran dan mulailah berbicara. Klik tombol lingkaran lagi setelah Anda selesai. Widget akan langsung mulai memutar kembali apa yang direkamnya.
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': …
Konversi file webm ke format wav yang dapat dipahami PyTorch.
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|>
Ringkasan dan langkah berikutnya
Dalam panduan ini, Anda telah mempelajari cara memproses audio menggunakan model Gemma 4. Contoh-contoh tersebut menunjukkan cara melakukan Speech-to-Text (ASR) untuk mentranskripsikan bahasa lisan, serta Terjemahan Ucapan Otomatis (AST) untuk menerjemahkan audio lisan langsung ke bahasa lain. Anda juga telah melihat cara merekam audio dari mikrofon di lingkungan notebook untuk diproses.
Lihat dokumentasi berikut untuk membaca lebih lanjut.