[Olewave's Review] AudioLM: a Language Modeling Approach to Audio Generation

Описание: Eager to train your own #Whisper or #GPT-4o model but running out of data? We are proud to offer this unique large-scale conversational speech dataset in different languages and topics for #ASR, #TTS, #NLP, and other conversational AI R&D. It has speaker labels and high quality transcriptions. The duration of the dataset depends on the customer's needs and can extend up to 1 million hours. See the description and samples in the following post:
  / olewave-large-scaled-convesational-speech-...  
send an email to [email protected] for more details.

AudioLM: a Language Modeling Approach to Audio Generation

https://arxiv.org/abs/2209.03143
Abstract:

We introduce AudioLM, a framework for high-quality audio generation with long-term consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space. We show how existing audio tokenizers provide different trade-offs between reconstruction quality and long-term structure, and we propose a hybrid tokenization scheme to achieve both objectives. Namely, we leverage the discretized activations of a masked language model pre-trained on audio to capture long-term structure and the discrete codes produced by a neural audio codec to achieve high-quality synthesis. By training on large corpora of raw audio waveforms, AudioLM learns to generate natural and coherent continuations given short prompts. When trained on speech, and without any transcript or annotation, AudioLM generates syntactically and semantically plausible speech continuations while also maintaining speaker identity and prosody for unseen speakers. Furthermore, we demonstrate how our approach extends beyond speech by generating coherent piano music continuations, despite being trained without any symbolic representation of music.

#audiolm #google #openai #gpt3 #audiogeneration #textgeneration #soundstream