update app

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import torch
 import torchaudio
 import tempfile
-import logging
+
 import numpy as np
 from nemo.collections.tts.models import FastPitchModel
 from nemo.collections.tts.models import HifiGanModel
@@ -13,119 +13,26 @@ from transformers import pipeline
 # spec_generator_2 = MixerTTSModel.from_pretrained("tts_en_lj_mixerttsx")
 # model1 = HifiGanModel.from_pretrained(model_name="tts_en_lj_hifigan_ft_mixerttsx")
 
-spec_generator = FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
-spec_generator.eval()
-voc_model = HifiGanModel.from_pretrained(model_name="tts_en_hifitts_hifigan_ft_fastpitch")
-voc_model.eval()
-
-pipe = pipeline("text-to-speech", model="suno/bark-small")
 
 def greet(name):
     return "Hello " + name + "!!"
 
-def generate_tts(text: str, speaker: int = 0):
-    sr = 44100
-    parsed = spec_generator.parse(text)
-    spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker)
-    audio = voc_model.convert_spectrogram_to_audio(spec=spectrogram)
-    
-    return (sr, audio.squeeze(0).cpu().numpy())
-
 def run():
-    logging.basicConfig(level=logging.INFO)
-
-    with gr.Blocks() as demo:
-        gr.Markdown(
-            """
-            <h1 align="center">Balacoon🦝 Text-to-Speech</h1>
-            1. Write an utterance to generate,
-            2. Select the model to synthesize with
-            3. Select speaker
-            4. Hit "Generate" and listen to the result!
-            You can learn more about models available
-            [here](https://huggingface.co/balacoon/tts).
-            Visit [Balacoon website](https://balacoon.com/) for more info.
-            """
-        )
-        with gr.Row(variant="panel"):
-            text = gr.Textbox(label="Text", placeholder="Type something here...")
-
-        with gr.Row():
-            with gr.Column(variant="panel"):
-                repo_files = os.listdir(model_repo_dir)
-                model_files = [x for x in repo_files if x.endswith("_cpu.addon")]
-                model_name = gr.Dropdown(
-                    label="Model",
-                    choices=model_files,
-                )
-            with gr.Column(variant="panel"):
-                speaker = gr.Dropdown(label="Speaker", choices=[])
-
-            def set_model(model_name_str: str):
-                """
-                gets value from `model_name`. either
-                uses cached list of speakers for the given model name
-                or loads the addon and checks what are the speakers.
-                """
-                global model_to_speakers
-                if model_name_str in model_to_speakers:
-                    speakers = model_to_speakers[model_name_str]
-                else:
-                    global tts, cur_model_path, locker
-                    with locker:
-                        # need to load this model to learn the list of speakers
-                        model_path = os.path.join(model_repo_dir, model_name_str)
-                        if tts is not None:
-                            del tts
-                        tts = TTS(model_path)
-                        cur_model_path = model_path
-                        speakers = tts.get_speakers()
-                        model_to_speakers[model_name_str] = speakers
-                
-                value = speakers[-1]
-                return gr.Dropdown.update(
-                    choices=speakers, value=value, visible=True
-                )
-
-            model_name.change(set_model, inputs=model_name, outputs=speaker)
-
-        with gr.Row(variant="panel"):
-            generate = gr.Button("Generate")
-        with gr.Row(variant="panel"):
-            audio = gr.Audio()
-
-        def synthesize_audio(text_str: str, model_name_str: str, speaker_str: str):
-            """
-            gets utterance to synthesize from `text` Textbox
-            and speaker name from `speaker` dropdown list.
-            speaker name might be empty for single-speaker models.
-            Synthesizes the waveform and updates `audio` with it.
-            """
-            if not text_str or not model_name_str or not speaker_str:
-                logging.info("text, model name or speaker are not provided")
-                return None
-            expected_model_path = os.path.join(model_repo_dir, model_name_str)
-            global tts, cur_model_path, locker
-            with locker:
-                if expected_model_path != cur_model_path:
-                    # reload model
-                    if tts is not None:
-                        del tts
-                    tts = TTS(expected_model_path)
-                    cur_model_path = expected_model_path
-                if len(text_str) > 1024:
-                    # truncate the text
-                    text_str = text_str[:1024]
-                samples = tts.synthesize(text_str, speaker_str)
-            return gr.Audio.update(value=(tts.get_sampling_rate(), samples))
-
-        generate.click(synthesize_audio, inputs=[text, model_name, speaker], outputs=audio)
-
-    demo.queue(concurrency_count=1).launch()
-
 
+    spec_generator = FastPitchModel.from_pretrained("tts_en_fastpitch_multispeaker")
+    spec_generator.eval()
+    voc_model = HifiGanModel.from_pretrained(model_name="tts_en_hifitts_hifigan_ft_fastpitch")
+    voc_model.eval()
 
+    pipe = pipeline("text-to-speech", model="suno/bark-small")
 
+    def generate_tts(text: str, speaker: int = 0):
+        sr = 44100
+        parsed = spec_generator.parse(text)
+        spectrogram = spec_generator.generate_spectrogram(tokens=parsed, speaker=speaker)
+        audio = voc_model.convert_spectrogram_to_audio(spec=spectrogram)
+        
+        return (sr, audio.squeeze(0).cpu().numpy())
 
     demo = gr.Interface(
         fn=generate_tts,
@@ -134,9 +41,6 @@ def run():
         outputs=gr.Audio(label="Output", type="numpy"),
     )
 
-    with gr.Row(variant="panel"):
-        generate = gr.Button("Generate")
-
     demo.launch(server_name="0.0.0.0", server_port=7860)