Add tortoise weights

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+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+
+# Installer logs
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+
+# Unit test / coverage reports
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+# Translations
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+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
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+# Spyder project settings
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+# Rope project settings
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+# mkdocs documentation
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+
+# mypy
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+# Pyre type checker
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CITATION.cff

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+cff-version: 1.3.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Betker"
+  given-names: "James"
+  orcid: "https://orcid.org/my-orcid?orcid=0000-0003-3259-4862"
+title: "TorToiSe text-to-speech"
+version: 2.0
+date-released: 2022-04-28
+url: "https://github.com/neonbjb/tortoise-tts"

LICENSE

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+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -0,0 +1,115 @@
+---
+# Generated at 2026-01-29T20:36:13Z from templates/weights/README.md.j2
+license: other
+language:
+  - eng
+tags:
+  - tts
+  - text-to-speech
+  - speech-synthesis
+  - voice-cloning
+library_name: ttsdb
+pipeline_tag: text-to-speech
+base_model:
+  - jbetker/tortoise-tts-v2
+
+---
+
+# TorToise
+
+> **This is a mirror of the original weights for use with [TTSDB](https://github.com/ttsds/ttsdb).**
+> 
+> Original weights: [https://huggingface.co/jbetker/tortoise-tts-v2](https://huggingface.co/jbetker/tortoise-tts-v2)
+> Original code: [https://github.com/neonbjb/tortoise-tts.git](https://github.com/neonbjb/tortoise-tts.git)
+
+
+Tortoise TTS voice cloning model.
+
+
+
+## Original Work
+
+This model was created by the original authors. Please cite their work if you use this model:
+
+
+```bibtex
+@misc{betker2023betterspeechsynthesisscaling,
+  title={Better speech synthesis through scaling},
+  author={James Betker},
+  year={2023},
+  eprint={2305.07243},
+  archivePrefix={arXiv},
+  primaryClass={cs.SD},
+  url={https://arxiv.org/abs/2305.07243},
+}
+```
+
+
+
+**Papers:**
+
+- https://arxiv.org/abs/2305.07243
+
+
+
+## Installation
+
+```bash
+pip install ttsdb-tortoise
+```
+
+## Usage
+
+```python
+from ttsdb_tortoise import TorToise
+
+# Load the model (downloads weights automatically)
+model = TorToise(model_id="ttsds/TorToise")
+
+# Synthesize speech
+audio, sample_rate = model.synthesize(
+    text="Hello, this is a test of TorToise.",
+    reference_audio="path/to/reference.wav",
+    text_reference="Transcript of the reference audio.",
+    language="en",
+)
+
+# Save the output
+model.save_audio(audio, sample_rate, "output.wav")
+```
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Sample Rate** | 24000 Hz |
+| **Parameters** | 960M |
+| **Architecture** | Autoregressive, Diffusion, Language Modeling |
+| **Languages** | English |
+| **Release Date** | 2022-05-17 |
+
+
+### Training Data
+
+
+- [LibriTTS](https://www.openslr.org/60/)
+
+
+- [HifiTTS]()
+
+
+
+
+## License
+
+- **Weights:** Other (see original repository)
+- **Code:** Apache License 2.0
+
+Please refer to the original repositories for full license terms.
+
+## Links
+
+- **Original Code:** [https://github.com/neonbjb/tortoise-tts.git](https://github.com/neonbjb/tortoise-tts.git)
+- **Original Weights:** [https://huggingface.co/jbetker/tortoise-tts-v2](https://huggingface.co/jbetker/tortoise-tts-v2)
+- **TTSDB Package:** [ttsdb-tortoise](https://pypi.org/project/ttsdb-tortoise/)
+- **TTSDB GitHub:** [https://github.com/ttsds/ttsdb](https://github.com/ttsds/ttsdb)

api.py

@@ -0,0 +1,373 @@
+import argparse
+import os
+import random
+from urllib import request
+
+import torch
+import torch.nn.functional as F
+import progressbar
+import torchaudio
+
+from models.classifier import AudioMiniEncoderWithClassifierHead
+from models.cvvp import CVVP
+from models.diffusion_decoder import DiffusionTts
+from models.autoregressive import UnifiedVoice
+from tqdm import tqdm
+
+from models.arch_util import TorchMelSpectrogram
+from models.clvp import CLVP
+from models.vocoder import UnivNetGenerator
+from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel
+from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
+from utils.tokenizer import VoiceBpeTokenizer, lev_distance
+
+
+pbar = None
+
+
+def download_models(specific_models=None):
+    """
+    Call to download all the models that Tortoise uses.
+    """
+    MODELS = {
+        'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/autoregressive.pth',
+        'classifier.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/classifier.pth',
+        'clvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/clvp.pth',
+        'cvvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/cvvp.pth',
+        'diffusion_decoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/diffusion_decoder.pth',
+        'vocoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/hf/.models/vocoder.pth',
+    }
+    os.makedirs('.models', exist_ok=True)
+    def show_progress(block_num, block_size, total_size):
+        global pbar
+        if pbar is None:
+            pbar = progressbar.ProgressBar(maxval=total_size)
+            pbar.start()
+
+        downloaded = block_num * block_size
+        if downloaded < total_size:
+            pbar.update(downloaded)
+        else:
+            pbar.finish()
+            pbar = None
+    for model_name, url in MODELS.items():
+        if specific_models is not None and model_name not in specific_models:
+            continue
+        if os.path.exists(f'.models/{model_name}'):
+            continue
+        print(f'Downloading {model_name} from {url}...')
+        request.urlretrieve(url, f'.models/{model_name}', show_progress)
+        print('Done.')
+
+
+def pad_or_truncate(t, length):
+    """
+    Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
+    """
+    if t.shape[-1] == length:
+        return t
+    elif t.shape[-1] < length:
+        return F.pad(t, (0, length-t.shape[-1]))
+    else:
+        return t[..., :length]
+
+
+def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
+    """
+    Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
+    """
+    return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
+                           model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
+                           conditioning_free=cond_free, conditioning_free_k=cond_free_k)
+
+
+def format_conditioning(clip, cond_length=132300):
+    """
+    Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
+    """
+    gap = clip.shape[-1] - cond_length
+    if gap < 0:
+        clip = F.pad(clip, pad=(0, abs(gap)))
+    elif gap > 0:
+        rand_start = random.randint(0, gap)
+        clip = clip[:, rand_start:rand_start + cond_length]
+    mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
+    return mel_clip.unsqueeze(0).cuda()
+
+
+def fix_autoregressive_output(codes, stop_token, complain=True):
+    """
+    This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
+    trained on and what the autoregressive code generator creates (which has no padding or end).
+    This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
+    a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
+    and copying out the last few codes.
+
+    Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
+    """
+    # Strip off the autoregressive stop token and add padding.
+    stop_token_indices = (codes == stop_token).nonzero()
+    if len(stop_token_indices) == 0:
+        if complain:
+            print("No stop tokens found, enjoy that output of yours!")
+        return codes
+    else:
+        codes[stop_token_indices] = 83
+    stm = stop_token_indices.min().item()
+    codes[stm:] = 83
+    if stm - 3 < codes.shape[0]:
+        codes[-3] = 45
+        codes[-2] = 45
+        codes[-1] = 248
+
+    return codes
+
+
+def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_samples, temperature=1, verbose=True):
+    """
+    Uses the specified diffusion model to convert discrete codes into a spectrogram.
+    """
+    with torch.no_grad():
+        cond_mels = []
+        for sample in conditioning_samples:
+            # The diffuser operates at a sample rate of 24000 (except for the latent inputs)
+            sample = torchaudio.functional.resample(sample, 22050, 24000)
+            sample = pad_or_truncate(sample, 102400)
+            cond_mel = wav_to_univnet_mel(sample.to(latents.device), do_normalization=False)
+            cond_mels.append(cond_mel)
+        cond_mels = torch.stack(cond_mels, dim=1)
+
+        output_seq_len = latents.shape[1] * 4 * 24000 // 22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
+        output_shape = (latents.shape[0], 100, output_seq_len)
+        precomputed_embeddings = diffusion_model.timestep_independent(latents, cond_mels, output_seq_len, False)
+
+        noise = torch.randn(output_shape, device=latents.device) * temperature
+        mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
+                                      model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
+                                     progress=verbose)
+        return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
+
+
+def classify_audio_clip(clip):
+    """
+    Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
+    :param clip: torch tensor containing audio waveform data (get it from load_audio)
+    :return: True if the clip was classified as coming from Tortoise and false if it was classified as real.
+    """
+    download_models(['classifier.pth'])
+    classifier = AudioMiniEncoderWithClassifierHead(2, spec_dim=1, embedding_dim=512, depth=5, downsample_factor=4,
+                                                    resnet_blocks=2, attn_blocks=4, num_attn_heads=4, base_channels=32,
+                                                    dropout=0, kernel_size=5, distribute_zero_label=False)
+    classifier.load_state_dict(torch.load('.models/classifier.pth', map_location=torch.device('cpu')))
+    clip = clip.cpu().unsqueeze(0)
+    results = F.softmax(classifier(clip), dim=-1)
+    return results[0][0]
+
+
+class TextToSpeech:
+    """
+    Main entry point into Tortoise.
+    :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
+                                      GPU OOM errors. Larger numbers generates slightly faster.
+    """
+    def __init__(self, autoregressive_batch_size=16):
+        self.autoregressive_batch_size = autoregressive_batch_size
+        self.tokenizer = VoiceBpeTokenizer()
+        download_models()
+
+        self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
+                                      model_dim=1024,
+                                      heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
+                                      train_solo_embeddings=False,
+                                      average_conditioning_embeddings=True).cpu().eval()
+        self.autoregressive.load_state_dict(torch.load('.models/autoregressive.pth'))
+
+        self.clvp = CLVP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12,
+                         text_seq_len=350, text_heads=8,
+                         num_speech_tokens=8192, speech_enc_depth=12, speech_heads=8, speech_seq_len=430,
+                         use_xformers=True).cpu().eval()
+        self.clvp.load_state_dict(torch.load('.models/clvp.pth'))
+
+        self.cvvp = CVVP(model_dim=512, transformer_heads=8, dropout=0, mel_codes=8192, conditioning_enc_depth=8, cond_mask_percentage=0,
+                         speech_enc_depth=8, speech_mask_percentage=0, latent_multiplier=1).cpu().eval()
+        self.cvvp.load_state_dict(torch.load('.models/cvvp.pth'))
+
+        self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
+                                      in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
+                                      layer_drop=0, unconditioned_percentage=0).cpu().eval()
+        self.diffusion.load_state_dict(torch.load('.models/diffusion_decoder.pth'))
+
+        self.vocoder = UnivNetGenerator().cpu()
+        self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
+        self.vocoder.eval(inference=True)
+
+    def tts_with_preset(self, text, voice_samples, preset='fast', **kwargs):
+        """
+        Calls TTS with one of a set of preset generation parameters. Options:
+            'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
+            'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
+            'standard': Very good quality. This is generally about as good as you are going to get.
+            'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
+        """
+        # Use generally found best tuning knobs for generation.
+        kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
+                       #'typical_sampling': True,
+                       'top_p': .8,
+                       'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
+        # Presets are defined here.
+        presets = {
+            'ultra_fast': {'num_autoregressive_samples': 32, 'diffusion_iterations': 16, 'cond_free': False},
+            'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 32},
+            'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 128},
+            'high_quality': {'num_autoregressive_samples': 512, 'diffusion_iterations': 1024},
+        }
+        kwargs.update(presets[preset])
+        return self.tts(text, voice_samples, **kwargs)
+
+    def tts(self, text, voice_samples, k=1, verbose=True,
+            # autoregressive generation parameters follow
+            num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
+            typical_sampling=False, typical_mass=.9,
+            # CLVP & CVVP parameters
+            clvp_cvvp_slider=.5,
+            # diffusion generation parameters follow
+            diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,
+            **hf_generate_kwargs):
+        """
+        Produces an audio clip of the given text being spoken with the given reference voice.
+        :param text: Text to be spoken.
+        :param voice_samples: List of 2 or more ~10 second reference clips which should be torch tensors containing 22.05kHz waveform data.
+        :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP and CVVP models) clips are returned.
+        :param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
+        ~~AUTOREGRESSIVE KNOBS~~
+        :param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP+CVVP.
+               As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
+        :param temperature: The softmax temperature of the autoregressive model.
+        :param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
+        :param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
+                                   of long silences or "uhhhhhhs", etc.
+        :param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
+        :param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
+        :param typical_sampling: Turns typical sampling on or off. This sampling mode is discussed in this paper: https://arxiv.org/abs/2202.00666
+                                 I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but
+                                 could use some tuning.
+        :param typical_mass: The typical_mass parameter from the typical_sampling algorithm.
+        ~~CLVP-CVVP KNOBS~~
+        :param clvp_cvvp_slider: Controls the influence of the CLVP and CVVP models in selecting the best output from the autoregressive model.
+                                [0,1]. Values closer to 1 will cause Tortoise to emit clips that follow the text more. Values closer to
+                                0 will cause Tortoise to emit clips that more closely follow the reference clip (e.g. the voice sounds more
+                                similar).
+        ~~DIFFUSION KNOBS~~
+        :param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
+                                     the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
+                                     however.
+        :param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
+                          each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
+                          of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
+                          dramatically improves realism.
+        :param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
+                            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
+                            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
+        :param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
+                                      are the "mean" prediction of the diffusion network and will sound bland and smeared.
+        ~~OTHER STUFF~~
+        :param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
+                                   Extra keyword args fed to this function get forwarded directly to that API. Documentation
+                                   here: https://huggingface.co/docs/transformers/internal/generation_utils
+        :return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
+                 Sample rate is 24kHz.
+        """
+        text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
+        text = F.pad(text, (0, 1))  # This may not be necessary.
+
+        conds = []
+        if not isinstance(voice_samples, list):
+            voice_samples = [voice_samples]
+        for vs in voice_samples:
+            conds.append(format_conditioning(vs))
+        conds = torch.stack(conds, dim=1)
+
+        diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
+
+        with torch.no_grad():
+            samples = []
+            num_batches = num_autoregressive_samples // self.autoregressive_batch_size
+            stop_mel_token = self.autoregressive.stop_mel_token
+            calm_token = 83  # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
+            self.autoregressive = self.autoregressive.cuda()
+            if verbose:
+                print("Generating autoregressive samples..")
+            for b in tqdm(range(num_batches), disable=not verbose):
+                codes = self.autoregressive.inference_speech(conds, text,
+                                                             do_sample=True,
+                                                             top_p=top_p,
+                                                             temperature=temperature,
+                                                             num_return_sequences=self.autoregressive_batch_size,
+                                                             length_penalty=length_penalty,
+                                                             repetition_penalty=repetition_penalty,
+                                                             max_generate_length=max_mel_tokens,
+                                                             **hf_generate_kwargs)
+                padding_needed = max_mel_tokens - codes.shape[1]
+                codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
+                samples.append(codes)
+            self.autoregressive = self.autoregressive.cpu()
+
+            clip_results = []
+            self.clvp = self.clvp.cuda()
+            self.cvvp = self.cvvp.cuda()
+            if verbose:
+                print("Computing best candidates using CLVP and CVVP")
+            for batch in tqdm(samples, disable=not verbose):
+                for i in range(batch.shape[0]):
+                    batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
+                clvp = self.clvp(text.repeat(batch.shape[0], 1), batch, return_loss=False)
+                cvvp_accumulator = 0
+                for cl in range(conds.shape[1]):
+                    cvvp_accumulator = cvvp_accumulator + self.cvvp(conds[:, cl].repeat(batch.shape[0], 1, 1), batch, return_loss=False )
+                cvvp = cvvp_accumulator / conds.shape[1]
+                clip_results.append(clvp * clvp_cvvp_slider + cvvp * (1-clvp_cvvp_slider))
+            clip_results = torch.cat(clip_results, dim=0)
+            samples = torch.cat(samples, dim=0)
+            best_results = samples[torch.topk(clip_results, k=k).indices]
+            self.clvp = self.clvp.cpu()
+            self.cvvp = self.cvvp.cpu()
+            del samples
+
+            # The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
+            # inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
+            # results, but will increase memory usage.
+            self.autoregressive = self.autoregressive.cuda()
+            best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
+                                               torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
+                                               return_latent=True, clip_inputs=False)
+            self.autoregressive = self.autoregressive.cpu()
+
+            if verbose:
+                print("Transforming autoregressive outputs into audio..")
+            wav_candidates = []
+            self.diffusion = self.diffusion.cuda()
+            self.vocoder = self.vocoder.cuda()
+            for b in range(best_results.shape[0]):
+                codes = best_results[b].unsqueeze(0)
+                latents = best_latents[b].unsqueeze(0)
+
+                # Find the first occurrence of the "calm" token and trim the codes to that.
+                ctokens = 0
+                for k in range(codes.shape[-1]):
+                    if codes[0, k] == calm_token:
+                        ctokens += 1
+                    else:
+                        ctokens = 0
+                    if ctokens > 8:  # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
+                        latents = latents[:, :k]
+                        break
+
+                mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, voice_samples, temperature=diffusion_temperature, verbose=verbose)
+                wav = self.vocoder.inference(mel)
+                wav_candidates.append(wav.cpu())
+            self.diffusion = self.diffusion.cpu()
+            self.vocoder = self.vocoder.cpu()
+
+            if len(wav_candidates) > 1:
+                return wav_candidates
+            return wav_candidates[0]

checkpoints/tacotron_symbols/.gitattributes

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checkpoints/tacotron_symbols/README.md

@@ -0,0 +1,3 @@
+---
+license: apache-2.0
+---

checkpoints/tacotron_symbols/special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

checkpoints/tacotron_symbols/tokenizer_config.json

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checkpoints/tacotron_symbols/vocab.json

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checkpoints/wav2vec2-large-960h/.gitattributes

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checkpoints/wav2vec2-large-960h/README.md

@@ -0,0 +1,92 @@
+---
+language: en
+datasets:
+- librispeech_asr
+tags:
+- speech
+
+license: apache-2.0
+---
+
+# Wav2Vec2-Large-960h
+
+[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
+
+The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model
+make sure that your speech input is also sampled at 16Khz.
+
+[Paper](https://arxiv.org/abs/2006.11477)
+
+Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli
+
+**Abstract**
+
+We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data.
+
+The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
+
+
+# Usage
+
+To transcribe audio files the model can be used as a standalone acoustic model as follows:
+
+```python
+ from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+ from datasets import load_dataset
+ import torch
+ 
+ # load model and processor
+ processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
+ model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
+     
+ # load dummy dataset and read soundfiles
+ ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+ 
+ # tokenize
+ input_values = processor(ds[0]["audio"]["array"],, return_tensors="pt", padding="longest").input_values  # Batch size 1
+ 
+ # retrieve logits
+ logits = model(input_values).logits
+ 
+ # take argmax and decode
+ predicted_ids = torch.argmax(logits, dim=-1)
+ transcription = processor.batch_decode(predicted_ids)
+ ```
+ 
+## Evaluation
+ 
+This code snippet shows how to evaluate **facebook/wav2vec2-large-960h** on LibriSpeech's "clean" and "other" test data.
+ 
+```python
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import soundfile as sf
+import torch
+from jiwer import wer
+
+
+librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
+
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda")
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
+
+def map_to_pred(batch):
+    input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values
+    with torch.no_grad():
+        logits = model(input_values.to("cuda")).logits
+
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+    batch["transcription"] = transcription
+    return batch
+
+result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
+
+print("WER:", wer(result["text"], result["transcription"]))
+```
+
+*Result (WER)*:
+
+| "clean" | "other" |
+|---|---|
+| 2.8 | 6.3 |

checkpoints/wav2vec2-large-960h/config.json

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+{
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
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+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
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+  ],
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+    5,
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+    2,
+    2,
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+    2
+  ],
+  "do_stable_layer_norm": false,
+  "feat_extract_activation": "gelu",
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+  "model_type": "wav2vec2",
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+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "transformers_version": "4.3.0.dev0",
+  "vocab_size": 32
+}

checkpoints/wav2vec2-large-960h/preprocessor_config.json

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+  "do_normalize": true,
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": false,
+  "sampling_rate": 16000
+}

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checkpoints/wav2vec2-large-960h/special_tokens_map.json

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checkpoints/wav2vec2-large-960h/tokenizer_config.json

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checkpoints/wav2vec2-large-960h/vocab.json

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checkpoints/wav2vec2-large-robust-ft-libritts-voxpopuli/README.md

@@ -0,0 +1,11 @@
+This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody.
+
+This model was created by fine-tuning the `facebook/wav2vec2-large-robust-ft-libri-960h` checkpoint on the [libritts](https://research.google/tools/datasets/libri-tts/) and [voxpopuli](https://github.com/facebookresearch/voxpopuli) datasets with a new vocabulary that includes punctuation.
+
+The model gets a respectable WER of 4.45% on the librispeech validation set. The baseline, `facebook/wav2vec2-large-robust-ft-libri-960h`, got 4.3%.
+
+Since the model was fine-tuned on clean audio, it is not well-suited for noisy audio like CommonVoice (though I may upload a checkpoint for that soon too). It still does pretty good, though.
+
+The vocabulary is uploaded to the model hub as well `jbetker/tacotron_symbols`.
+
+Check out my speech transcription script repo, [ocotillo](https://github.com/neonbjb/ocotillo) for usage examples: https://github.com/neonbjb/ocotillo

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+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
+  "output_hidden_size": 1024,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 768,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
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+    512,
+    512,
+    512,
+    1500
+  ],
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+    3,
+    3,
+    1,
+    1
+  ],
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+  "transformers_version": "4.16.2",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 148,
+  "xvector_output_dim": 512
+}

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data/riding_hood.txt

@@ -0,0 +1,54 @@
+Once upon a time there lived in a certain village a little country girl, the prettiest creature who was ever seen. Her mother was excessively fond of her; and her grandmother doted on her still more. This good woman had a little red riding hood made for her. It suited the girl so extremely well that everybody called her Little Red Riding Hood.
+One day her mother, having made some cakes, said to her, "Go, my dear, and see how your grandmother is doing, for I hear she has been very ill. Take her a cake, and this little pot of butter."
+
+Little Red Riding Hood set out immediately to go to her grandmother, who lived in another village.
+
+As she was going through the wood, she met with a wolf, who had a very great mind to eat her up, but he dared not, because of some woodcutters working nearby in the forest. He asked her where she was going. The poor child, who did not know that it was dangerous to stay and talk to a wolf, said to him, "I am going to see my grandmother and carry her a cake and a little pot of butter from my mother."
+
+"Does she live far off?" said the wolf
+
+"Oh I say," answered Little Red Riding Hood; "it is beyond that mill you see there, at the first house in the village."
+
+"Well," said the wolf, "and I'll go and see her too. I'll go this way and go you that, and we shall see who will be there first."
+
+The wolf ran as fast as he could, taking the shortest path, and the little girl took a roundabout way, entertaining herself by gathering nuts, running after butterflies, and gathering bouquets of little flowers. It was not long before the wolf arrived at the old woman's house. He knocked at the door: tap, tap.
+
+"Who's there?"
+
+"Your grandchild, Little Red Riding Hood," replied the wolf, counterfeiting her voice; "who has brought you a cake and a little pot of butter sent you by mother."
+
+The good grandmother, who was in bed, because she was somewhat ill, cried out, "Pull the bobbin, and the latch will go up."
+
+The wolf pulled the bobbin, and the door opened, and then he immediately fell upon the good woman and ate her up in a moment, for it been more than three days since he had eaten. He then shut the door and got into the grandmother's bed, expecting Little Red Riding Hood, who came some time afterwards and knocked at the door: tap, tap.
+
+"Who's there?"
+
+Little Red Riding Hood, hearing the big voice of the wolf, was at first afraid; but believing her grandmother had a cold and was hoarse, answered, "It is your grandchild Little Red Riding Hood, who has brought you a cake and a little pot of butter mother sends you."
+
+The wolf cried out to her, softening his voice as much as he could, "Pull the bobbin, and the latch will go up."
+
+Little Red Riding Hood pulled the bobbin, and the door opened.
+
+The wolf, seeing her come in, said to her, hiding himself under the bedclothes, "Put the cake and the little pot of butter upon the stool, and come get into bed with me."
+
+Little Red Riding Hood took off her clothes and got into bed. She was greatly amazed to see how her grandmother looked in her nightclothes, and said to her, "Grandmother, what big arms you have!"
+
+"All the better to hug you with, my dear."
+
+"Grandmother, what big legs you have!"
+
+"All the better to run with, my child."
+
+"Grandmother, what big ears you have!"
+
+"All the better to hear with, my child."
+
+"Grandmother, what big eyes you have!"
+
+"All the better to see with, my child."
+
+"Grandmother, what big teeth you have got!"
+
+"All the better to eat you up with."
+
+And, saying these words, this wicked wolf fell upon Little Red Riding Hood, and ate her all up.

data/tokenizer.json

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do_tts.py

@@ -0,0 +1,34 @@
+import argparse
+import os
+
+import torchaudio
+
+from api import TextToSpeech
+from utils.audio import load_audio, get_voices
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
+                        help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
+                        default=.5)
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
+    args = parser.parse_args()
+    os.makedirs(args.output_path, exist_ok=True)
+
+    tts = TextToSpeech()
+
+    voices = get_voices()
+    selected_voices = args.voice.split(',')
+    for voice in selected_voices:
+        cond_paths = voices[voice]
+        conds = []
+        for cond_path in cond_paths:
+            c = load_audio(cond_path, 22050)
+            conds.append(c)
+        gen = tts.tts_with_preset(args.text, conds, preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
+        torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)
+