Update README.md

README.md

@@ -9,46 +9,50 @@ language:
 pipeline_tag: text-to-speech
 ---
 
+# Parler-TTS v0.1
+
 <a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts_mini">
   <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
 </a>
 
-# Parler-TTS v0.1
-
-**Parler-TTS v0.1** is a lightweight text-to-speech (TTS) model, trained on 10.5K 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)
+**Parler-TTS v0.1** is a lightweight text-to-speech (TTS) model, trained on 10.5K 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).
+It is the first release model from 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.
 
 ## Usage
 
 Using Parler-TTS is as simple as "bonjour". Simply install the library once:
+
 ```sh
 pip install git+https://github.com/huggingface/parler-tts.git
 ```
 
- You can then use the model with the following inference snippet:
+You can then use the model with the following inference snippet:
 
 ```py
+import torch
 from parler_tts import ParlerTTSForConditionalGeneration
-from transformers import AutoTokenizer, AutoFeatureExtractor
+from transformers import AutoTokenizer
 import soundfile as sf
 
-model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_300M_v0.1")
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_300M_v0.1").to(device)
 tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_300M_v0.1")
 
 prompt = "Hey, how are you doing today?"
 description = "A female speaker with a slightly low-pitched voice delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks very fast."
 
-input_ids = tokenizer(description, return_tensors="pt").input_ids
-prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+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**:
 * 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
+* 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
@@ -90,4 +94,3 @@ If you found this repository useful, please consider citing this work and also t
 ## License
 
 This model is permissively licensed under the Apache 2.0 license.
-