Tiny Narrations

3.5 years worth of synthetically narrated children's stories. Scripts written by GPT4 from TinyStories.

Release page

Listen to a sample.

Instructions

git clone https://github.com/sfcompute/tinynarrations.git
cd ./tinynarrations
pip install boto3

Then login to AWS. Transfer to other AWS instances is free, otherwise expensive egress for the larger splits.

Edit line 20 to specify the data splits you'd like to download.

"""
Folders:
/train_data - ~14TB, ~90k wav files
/val_data - ~137GB, 864 wav files

/train_data_semantic_tokenized - ~56GB, ~90k pt files
/val_data_semantic_tokenized - ~573MB, 864 pt files

/train_data_encodec_tokenized - ~687GB, ~90k pt files
/val_data_encodec_tokenized - ~7GB, 864 pt files

"""

folders = ['val_data_encodec_tokenized'] # ADD FOLDERS HERE

There are two main raw audio folders, /train_data and /val_data, which contain synthetically generated wav files. Batch inference scripts are included in the generations folder. For smaller downloads, pretokenized data is available (val_data_semantic_tokenized, val_data_encodec_tokenized etc.), with Hubert for semantic tokens and Encodec for decodable audio tokens, assuming an AudioLM style approach.

To decode tokenized Encodec data:

encodec = EncodecModel.encodec_model_24khz().cuda()
encodec.set_target_bandwidth(6.0)

encoded_tokens = torch.load(path)
print(encoded_tokens.shape) # (n 1-secs, 8 quantizers, 75 tps)
frame_list = [(frame.unsqueeze(0).cuda(), None) for frame in sample_encoded_tokens]
print(frame_list[0][0].shape)
encodec.segment = 1

with torch.no_grad():
decoded_waveform = encodec.decode(frame_list)

Generation

As of now we don't have standardized scripts for generation of similar datasets. The main bit is just a batch inference function. To run batch inference on XTTS-v2, we used the following modified class method and the original TTS library:

def batch_inference(self, text_tokens, gpt_cond_latent, speaker_embedding,
        temperature=0.75, length_penalty=1.0, repetition_penalty=10.0, top_k=50, top_p=0.85, do_sample=True, num_beams=1, speed=1.0, **hf_generate_kwargs,
    ):
        wavs = []
        gpt_latents_list = []

        with torch.no_grad():
            with torch.autocast(device_type='cuda', dtype=torch.bfloat16) if autocast else contextlib.nullcontext():
                gpt_codes = self.gpt.generate(
                    cond_latents=gpt_cond_latent,
                    text_inputs=text_tokens,
                    input_tokens=None,
                    do_sample=do_sample,
                    # etc. (gpt_batch_size is 1, we're hacking around it.)
                    top_p=top_p, top_k=top_k, temperature=temperature, num_return_sequences=self.gpt_batch_size, num_beams=num_beams, length_penalty=length_penalty, repetition_penalty=repetition_penalty, output_attentions=False, **hf_generate_kwargs,
                )
                expected_output_len = torch.tensor(
                    [gpt_codes.shape[-1] * self.gpt.code_stride_len], device=text_tokens.device
                )

                text_len = torch.tensor([text_tokens.shape[-1]], device=self.device)
                gpt_latents = self.gpt(
                    text_tokens,
                    text_len,
                    gpt_codes,
                    expected_output_len,
                    cond_latents=gpt_cond_latent,
                    return_attentions=False,
                    return_latent=True,
                )

                if speed != 1.0:
                    gpt_latents = F.interpolate(
                        gpt_latents.transpose(1, 2), scale_factor=(1.0 / max(speed, 0.05)), mode='linear'
                    ).transpose(1, 2)

                wav = self.hifigan_decoder(gpt_latents, g=speaker_embedding)

            gpt_latents_list.append(gpt_latents.cpu().float())
            wavs.append(wav.squeeze().float())

        return {
            'wav': torch.cat(wavs, dim=0),
            'gpt_latents': torch.cat(gpt_latents_list, dim=1).numpy(),
            'gpt_codes': gpt_codes.cpu().numpy(),
            'speaker_embedding': speaker_embedding,
        }