create demo

README.md

@@ -1,5 +1,5 @@
 ---
-title: Parler Tts Expresso V0.1
+title: Parler TTS: Expresso v0.1
 emoji: 📉
 colorFrom: red
 colorTo: red

app.py

@@ -0,0 +1,173 @@
+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"
+
+# TODO(SG): update to the latest checkpoint
+repo_id = "reach-vb/parler-tts-expresso-mistral-v0.1"
+
+model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).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 = "*Remember* - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of *five times*."
+default_description = "Thomas speaks with emphasis at a moderate pace with high quality."
+examples = [
+    [
+        "Remember - this is only the first iteration of the model! To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data by a factor of five times.",
+        "Thomas speaks sadly at a very slow pace with high quality."
+    ],
+    [
+        "Shhh! Did you know? You can reproduce this entire training recipe by following the steps outlined on the model card. It only takes one hour to train!",
+        "Talia whispers quickly with high quality audio.",
+    ],
+    [
+        "But that's no secret! The entire project is open-source first. We are releasing all datasets, training and inference code, so that you can use them yourself!",
+        "Elisabeth speaks happily at a slightly slower than average pace with high quality audio.",
+    ],
+    [
+        "Hey there. I'm Jerry. Or at least, I *think* I am? I just need to check that quickly.",
+        "Jerry speaks in a confused tone at a moderate pace with high quality audio.",
+    ],
+]
+
+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):
+    inputs = tokenizer(description, return_tensors="pt").to(device)
+    prompt = tokenizer(preprocess(text), return_tensors="pt").to(device)
+
+    set_seed(SEED)
+    generation = model.generate(
+        input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids, 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: Expresso v0.1 ☕️️
+                </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. The model demonstrated here, <a href="https://huggingface.co/parler-tts/parler_tts_mini_expresso_v0.1"> Parler-TTS Mini: Expresso v0.1</a>, 
+        is fine-tuned on the <a href="https://huggingface.co/datasets/ylacombe/expresso"> Expresso dataset</a>.
+        It generates high-quality speech in a given <b>emotion</b> and <b>voice</b> that can be controlled through a simple text prompt.</p>
+
+        <p>Tips for ensuring good generation:
+        <ul>
+            <li>Specify the name of a male speaker (Jerry, Thomas) or female speaker (Talia, Elisabeth) for consistent voices</li>
+            <li>The model can generate in a range of emotions, including: "happy", "confused", "default" (meaning no particular emotion conveyed), "laughing", "sad", "whisper", "emphasis"</li>
+            <li>Include the term "high quality 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>Wrap words in asterisk to emphasise them (e.g. `*Remember*` in the example below)</li>
+        </ul>
+        </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")
+            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]
+    outputs = [audio_out]
+    gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True)
+    run_button.click(fn=gen_tts, inputs=inputs, outputs=outputs, queue=True)
+    gr.HTML(
+        """
+        <p>To improve the prosody and naturalness of the speech further, we're scaling up the amount of training data to 50k hours of speech.
+        The v1 release of the model will be trained on this data, as well as inference optimisations, such as flash attention
+        and torch compile, that will improve the latency by 2-4x. 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. 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)

requirements.txt

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+git+https://github.com/huggingface/parler-tts.git
+accelerate