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README.md

@@ -1,150 +1,5 @@
----
-library_name: transformers
-tags:
-- text-to-speech
-- annotation
-license: apache-2.0
-base_model:
-- parler-tts/parler-tts-mini-v1
-language:
-- en
-pipeline_tag: text-to-speech
-inference: false
-datasets:
-- parler-tts/mls_eng
-- parler-tts/libritts_r_filtered
-- parler-tts/libritts-r-filtered-speaker-descriptions
-- parler-tts/mls-eng-speaker-descriptions
----
-
-
-<img src="https://huggingface.co/datasets/parler-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
-
-
-# Parler-TTS Mini v1
-
-<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
-  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
-</a>
-
-**Parler-TTS Mini v1** is a lightweight text-to-speech (TTS) model, trained on 45K hours of audio data, that can generate high-quality, natural sounding speech with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).
-
-With [Parler-TTS Large v1](https://huggingface.co/parler-tts/parler-tts-large-v1), this is the second set of models published as part of the [Parler-TTS](https://github.com/huggingface/parler-tts) project, which aims to provide the community with TTS training resources and dataset pre-processing code.
-
-## 📖 Quick Index
-* [👨‍💻 Installation](#👨‍💻-installation)
-* [🎲 Using a random voice](#🎲-random-voice)
-* [🎯 Using a specific speaker](#🎯-using-a-specific-speaker)
-* [Motivation](#motivation)
-* [Optimizing inference](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md)
-
-## 🛠️ Usage
-
-### 👨‍💻 Installation
-
-Using Parler-TTS is as simple as "bonjour". Simply install the library once:
-
-```sh
-pip install git+https://github.com/huggingface/parler-tts.git
-```
-
-### 🎲 Random voice
-
-
-**Parler-TTS** has been trained to generate speech with features that can be controlled with a simple text prompt, for example:
-
-```py
-import torch
-from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer
-import soundfile as sf
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
-tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
-
-prompt = "Hey, how are you doing today?"
-description = "A female speaker delivers a slightly expressive and animated speech with a moderate speed and pitch. The recording is of very high quality, with the speaker's voice sounding clear and very close up."
-
-input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
-prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-
-generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
-audio_arr = generation.cpu().numpy().squeeze()
-sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
-```
-
-### 🎯 Using a specific speaker
-
-To ensure speaker consistency across generations, this checkpoint was also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura).
-
-To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise.`
-
-```py
-import torch
-from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer
-import soundfile as sf
-
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
-model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
-tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
-
-prompt = "Hey, how are you doing today?"
-description = "Jon's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
-
-input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
-prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
-
-generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
-audio_arr = generation.cpu().numpy().squeeze()
-sf.write("parler_tts_out.wav", audio_arr, model.config.sampling_rate)
-```
-
-**Tips**:
-* We've set up an [inference guide](https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md) to make generation faster. Think SDPA, torch.compile, batching and streaming!
-* Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise
-* Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech
-* The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt
-
-## Motivation
-
-Parler-TTS is a reproduction of work from the paper [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://www.text-description-to-speech.com) by Dan Lyth and Simon King, from Stability AI and Edinburgh University respectively. 
-
-Contrarily to other TTS models, Parler-TTS is a **fully open-source** release. All of the datasets, pre-processing, training code and weights are released publicly under permissive license, enabling the community to build on our work and develop their own powerful TTS models.
-Parler-TTS was released alongside:
-* [The Parler-TTS repository](https://github.com/huggingface/parler-tts) - you can train and fine-tuned your own version of the model.
-* [The Data-Speech repository](https://github.com/huggingface/dataspeech) - a suite of utility scripts designed to annotate speech datasets.
-* [The Parler-TTS organization](https://huggingface.co/parler-tts) - where you can find the annotated datasets as well as the future checkpoints.
-
-## Citation
-
-If you found this repository useful, please consider citing this work and also the original Stability AI paper:
-
-```
-@misc{lacombe-etal-2024-parler-tts,
-  author = {Yoach Lacombe and Vaibhav Srivastav and Sanchit Gandhi},
-  title = {Parler-TTS},
-  year = {2024},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/huggingface/parler-tts}}
-}
-```
-
-```
-@misc{lyth2024natural,
-      title={Natural language guidance of high-fidelity text-to-speech with synthetic annotations},
-      author={Dan Lyth and Simon King},
-      year={2024},
-      eprint={2402.01912},
-      archivePrefix={arXiv},
-      primaryClass={cs.SD}
-}
-```
-
-## License
-
-This model is permissively licensed under the Apache 2.0 license.
+---
+license: apache-2.0
+language:
+- en
+---

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/fsx/yoach/tmp/artefacts/training-50K-mini-without-accents-3-mononode/",
+  "_name_or_path": "/fsx/yoach/tmp/artefacts/training-PES24-TTS-mini-v1/",
   "architectures": [
     "ParlerTTSForConditionalGeneration"
   ],
@@ -249,6 +249,6 @@
     "vocab_size": 32128
   },
   "torch_dtype": "float32",
-  "transformers_version": "4.40.2",
+  "transformers_version": "4.43.3",
   "vocab_size": 32128
 }

generation_config.json

@@ -2,11 +2,11 @@
   "_from_model_config": true,
   "bos_token_id": 1025,
   "decoder_start_token_id": 1025,
-  "min_new_tokens": 10,
   "do_sample": true,
   "eos_token_id": 1024,
   "guidance_scale": 1,
   "max_length": 2580,
+  "min_new_tokens": 10,
   "pad_token_id": 1024,
-  "transformers_version": "4.40.2"
+  "transformers_version": "4.43.3"
 }

model.safetensors

@@ -1,3 +1,3 @@
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