Create app.py

app.py

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+import spaces
+import gradio as gr
+import torch
+from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer
+from string import punctuation
+import re
+
+
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+
+repo_id =  "parler-tts/parler-tts-mini-v1"
+repo_id_large = "parler-tts/parler-tts-large-v1"
+
+model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device)
+model_large = ParlerTTSForConditionalGeneration.from_pretrained(repo_id_large).to(device)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
+
+
+SAMPLE_RATE = feature_extractor.sampling_rate
+SEED = 42
+
+default_text = "All of the data, pre-processing, training code, and weights are released publicly under a permissive license, enabling the community to build on our work and develop their own powerful models."
+default_description = "Laura's voice is monotone yet slightly fast in delivery, with a very close recording that almost has no background noise."
+examples = [
+    [
+        "This version introduces speaker consistency across generations, characterized by their name. For example, Jon, Lea, Gary, Jenna, Mike and Laura.",
+        "Gary's voice is monotone yet slightly fast in delivery, with a very close recording that has no background noise.",
+        None,
+    ],
+    [
+        '''There's 34 speakers. To take advantage of this, simply adapt your text description to specify which speaker to use: "Mike speaks animatedly...".''',
+        "Gary speaks slightly animatedly and slightly slowly in delivery, with a very close recording that has no background noise.",
+        None
+    ],
+    [
+        "'This is the best time of my life, Bartley,' she said happily.",
+        "A female speaker delivers a slightly expressive and animated speech with a moderate speed. The recording features a low-pitch voice and slight background noise, creating a close-sounding audio experience.",
+        None,
+    ],
+    [
+        "Montrose also, after having experienced still more variety of good and bad fortune, threw down his arms, and retired out of the kingdom.",
+        "A man voice speaks slightly slowly with very noisy background, carrying a low-pitch tone and displaying a touch of expressiveness and animation. The sound is very distant, adding an air of intrigue.",
+        None
+    ],
+    [
+        "Once upon a time, in the depth of winter, when the flakes of snow fell like feathers from the clouds, a queen sat sewing at her pal-ace window, which had a carved frame of black wood.",
+        "In a very poor recording quality, a female speaker delivers her slightly expressive and animated words with a fast pace. There's high level of background noise and a very distant-sounding reverberation. Her voice is slightly higher pitched than average.",
+        None,
+    ],
+]
+
+number_normalizer = EnglishNumberNormalizer()
+
+def preprocess(text):
+    text = number_normalizer(text).strip()
+    text = text.replace("-", " ")
+    if text[-1] not in punctuation:
+        text = f"{text}."
+    
+    abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b'
+    
+    def separate_abb(chunk):
+        chunk = chunk.replace(".","")
+        print(chunk)
+        return " ".join(chunk)
+    
+    abbreviations = re.findall(abbreviations_pattern, text)
+    for abv in abbreviations:
+        if abv in text:
+            text = text.replace(abv, separate_abb(abv))
+    return text
+
+@spaces.GPU
+def gen_tts(text, description, use_large=False):
+    inputs = tokenizer(description.strip(), return_tensors="pt").to(device)
+    prompt = tokenizer(preprocess(text), return_tensors="pt").to(device)
+
+    set_seed(SEED)
+    if use_large:
+        generation = model_large.generate(
+            input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, attention_mask=inputs.attention_mask, prompt_attention_mask=prompt.attention_mask, do_sample=True, temperature=1.0
+        )      
+    else:
+        generation = model.generate(
+            input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, attention_mask=inputs.attention_mask, prompt_attention_mask=prompt.attention_mask, do_sample=True, temperature=1.0
+        )
+    audio_arr = generation.cpu().numpy().squeeze()
+
+    return SAMPLE_RATE, audio_arr
+
+
+css = """
+        #share-btn-container {
+            display: flex;
+            padding-left: 0.5rem !important;
+            padding-right: 0.5rem !important;
+            background-color: #000000;
+            justify-content: center;
+            align-items: center;
+            border-radius: 9999px !important; 
+            width: 13rem;
+            margin-top: 10px;
+            margin-left: auto;
+            flex: unset !important;
+        }
+        #share-btn {
+            all: initial;
+            color: #ffffff;
+            font-weight: 600;
+            cursor: pointer;
+            font-family: 'IBM Plex Sans', sans-serif;
+            margin-left: 0.5rem !important;
+            padding-top: 0.25rem !important;
+            padding-bottom: 0.25rem !important;
+            right:0;
+        }
+        #share-btn * {
+            all: unset !important;
+        }
+        #share-btn-container div:nth-child(-n+2){
+            width: auto !important;
+            min-height: 0px !important;
+        }
+        #share-btn-container .wrap {
+            display: none !important;
+        }
+"""
+with gr.Blocks(css=css) as block:
+    gr.HTML(
+        """
+            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
+              <div
+                style="
+                  display: inline-flex; align-items: center; gap: 0.8rem; font-size: 1.75rem;
+                "
+              >
+                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
+                  Parler-TTS 🗣️
+                </h1>
+              </div>
+            </div>
+        """
+    )
+    gr.HTML(
+        f"""
+        <p><a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> is a training and inference library for
+        high-fidelity text-to-speech (TTS) models.</p> 
+        <p>The models demonstrated here, Parler-TTS <a href="https://huggingface.co/parler-tts/parler-tts-mini-v1">Mini v1</a> and <a href="https://huggingface.co/parler-tts/parler-tts-large-v1">Large v1</a>, 
+        are trained using 45k hours of narrated English audiobooks. It generates high-quality speech 
+        with features that can be controlled using a simple text prompt (e.g. gender, background noise, speaking rate, pitch and reverberation).</p>
+        <p>By default, Parler-TTS generates 🎲 random voice. To ensure 🎯 <b> speaker consistency </b> across generations, these checkpoints were also trained on 34 speakers, characterized by name (e.g. Jon, Lea, Gary, Jenna, Mike, Laura). Learn more about this <a href="https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md#speaker-consistency"> here </a>.</p>
+        
+        <p>To take advantage of this, simply adapt your text description to specify which speaker to use: `Jon's voice is monotone...`</p>
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text", lines=2, value=default_text, elem_id="input_text")
+            description = gr.Textbox(label="Description", lines=2, value=default_description, elem_id="input_description")
+            use_large = gr.Checkbox(value=False, label="Use Large checkpoint", info="Generate with Parler-TTS Large v1 instead of Mini v1 - Better but way slower.")
+            run_button = gr.Button("Generate Audio", variant="primary")
+        with gr.Column():
+            audio_out = gr.Audio(label="Parler-TTS generation", type="numpy", elem_id="audio_out")
+
+    inputs = [input_text, description, use_large]
+    outputs = [audio_out]
+    run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
+    gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
+    gr.HTML(
+        """
+        <p>Tips for ensuring good generation:
+        <ul>
+            <li>Include the term "very clear audio" to generate the highest quality audio, and "very noisy audio" for high levels of background noise</li>
+            <li>Punctuation can be used to control the prosody of the generations, e.g. use commas to add small breaks in speech</li>
+            <li>The remaining speech features (gender, speaking rate, pitch and reverberation) can be controlled directly through the prompt</li>
+        </ul>
+        </p>
+        <p>Parler-TTS can be much faster. We give some tips on how to generate much more quickly in this <a href="https://github.com/huggingface/parler-tts/blob/main/INFERENCE.md"> inference guide</a>. Think SDPA, torch.compile, batching and streaming!</p>
+        
+        <p>If you want to find out more about how this model was trained and even fine-tune it yourself, check-out the 
+        <a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a> repository on GitHub.</p>
+        
+        <p>The Parler-TTS codebase and its associated checkpoints are licensed under <a href='https://github.com/huggingface/parler-tts?tab=Apache-2.0-1-ov-file#readme'> Apache 2.0</a>.</p>
+        """
+    )
+
+
+block.queue()
+block.launch(share=True)