Delete app.py

app.py

@@ -1,263 +0,0 @@
-import spaces
-import os
-
-import gradio as gr
-import numpy as np
-
-import torch
-import torchaudio
-from generator import Segment, load_csm_1b
-from huggingface_hub import hf_hub_download, login
-from watermarking import watermark
-
-api_key = os.getenv("HF_TOKEN")
-gpu_timeout = int(os.getenv("GPU_TIMEOUT", 60))
-CSM_1B_HF_WATERMARK = list(map(int, os.getenv("WATERMARK_KEY").split(" ")))
-
-login(token=api_key)
-
-SPACE_INTRO_TEXT = """\
-# Sesame CSM 1B
-
-Generate from CSM 1B (Conversational Speech Model). 
-Code is available on GitHub: [SesameAILabs/csm](https://github.com/SesameAILabs/csm). 
-Checkpoint is [hosted on HuggingFace](https://huggingface.co/sesame/csm-1b).
-
-Try out our interactive demo [sesame.com/voicedemo](https://www.sesame.com/voicedemo), 
-this uses a fine-tuned variant of CSM.
-
-The model has some capacity for non-English languages due to data contamination in the training 
-data, but it is likely not to perform well.
-
----
-
-"""
-
-CONVO_INTRO_TEXT = """\
-## Conversation content
-
-Each line is an utterance in the conversation to generate. Speakers alternate between A and B, starting with speaker A.
-"""
-
-DEFAULT_CONVERSATION = """\
-Hey how are you doing.
-Pretty good, pretty good.
-I'm great, so happy to be speaking to you.
-Me too, this is some cool stuff huh?
-Yeah, I've been reading more about speech generation, and it really seems like context is important.
-Definitely.
-"""
-
-SPEAKER_PROMPTS = {
-    "conversational_a": {
-        "text": (
-            "like revising for an exam I'd have to try and like keep up the momentum because I'd "
-            "start really early I'd be like okay I'm gonna start revising now and then like "
-            "you're revising for ages and then I just like start losing steam I didn't do that "
-            "for the exam we had recently to be fair that was a more of a last minute scenario "
-            "but like yeah I'm trying to like yeah I noticed this yesterday that like Mondays I "
-            "sort of start the day with this not like a panic but like a"
-        ),
-        "audio": "prompts/conversational_a.wav",
-    },
-    "conversational_b": {
-        "text": (
-            "like a super Mario level. Like it's very like high detail. And like, once you get "
-            "into the park, it just like, everything looks like a computer game and they have all "
-            "these, like, you know, if, if there's like a, you know, like in a Mario game, they "
-            "will have like a question block. And if you like, you know, punch it, a coin will "
-            "come out. So like everyone, when they come into the park, they get like this little "
-            "bracelet and then you can go punching question blocks around."
-        ),
-        "audio": "prompts/conversational_b.wav",
-    },
-    "read_speech_a": {
-        "text": (
-            "And Lake turned round upon me, a little abruptly, his odd yellowish eyes, a little "
-            "like those of the sea eagle, and the ghost of his smile that flickered on his "
-            "singularly pale face, with a stern and insidious look, confronted me."
-        ),
-        "audio": "prompts/read_speech_a.wav",
-    },
-    "read_speech_b": {
-        "text": (
-            "He was such a big boy that he wore high boots and carried a jack knife. He gazed and "
-            "gazed at the cap, and could not keep from fingering the blue tassel."
-        ),
-        "audio": "prompts/read_speech_b.wav",
-    },
-    "read_speech_c": {
-        "text": (
-            "All passed so quickly, there was so much going on around him, the Tree quite forgot "
-            "to look to himself."
-        ),
-        "audio": "prompts/read_speech_c.wav",
-    },
-    "read_speech_d": {
-        "text": (
-            "Suddenly I was back in the old days Before you felt we ought to drift apart. It was "
-            "some trick-the way your eyebrows raise."
-        ),
-        "audio": "prompts/read_speech_d.wav",
-    },
-}
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-generator = load_csm_1b(device=device)
-
-
-@spaces.GPU(duration=gpu_timeout)
-def infer(
-    text_prompt_speaker_a,
-    text_prompt_speaker_b,
-    audio_prompt_speaker_a,
-    audio_prompt_speaker_b,
-    gen_conversation_input,
-) -> tuple[np.ndarray, int]:
-    """Generates an audio conversation between two speakers """
-    
-    # Estimate token limit, otherwise failure might happen after many utterances have been generated.
-    if len(gen_conversation_input.strip() + text_prompt_speaker_a.strip() + text_prompt_speaker_b.strip()) >= 2000:
-        raise gr.Error("Prompts and conversation too long.", duration=30)
-
-    try:
-        return _infer(
-            text_prompt_speaker_a,
-            text_prompt_speaker_b,
-            audio_prompt_speaker_a,
-            audio_prompt_speaker_b,
-            gen_conversation_input,
-        )
-    except ValueError as e:
-        raise gr.Error(f"Error generating audio: {e}", duration=120)
-
-
-def _infer(
-    text_prompt_speaker_a,
-    text_prompt_speaker_b,
-    audio_prompt_speaker_a,
-    audio_prompt_speaker_b,
-    gen_conversation_input,
-) -> tuple[np.ndarray, int]:
-    audio_prompt_a = prepare_prompt(text_prompt_speaker_a, 0, audio_prompt_speaker_a)
-    audio_prompt_b = prepare_prompt(text_prompt_speaker_b, 1, audio_prompt_speaker_b)
-
-    prompt_segments: list[Segment] = [audio_prompt_a, audio_prompt_b]
-    generated_segments: list[Segment] = []
-
-    conversation_lines = [line.strip() for line in gen_conversation_input.strip().split("\n") if line.strip()]
-    for i, line in enumerate(conversation_lines):
-        # Alternating speakers A and B, starting with A
-        speaker_id = i % 2
-
-        audio_tensor = generator.generate(
-            text=line,
-            speaker=speaker_id,
-            context=prompt_segments + generated_segments,
-            max_audio_length_ms=30_000,
-        )
-        generated_segments.append(Segment(text=line, speaker=speaker_id, audio=audio_tensor))
-
-    # Concatenate all generations and convert to 16-bit int format
-    audio_tensors = [segment.audio for segment in generated_segments]
-    audio_tensor = torch.cat(audio_tensors, dim=0)
-
-    # This applies an imperceptible watermark to identify audio as AI-generated.
-    # Watermarking ensures transparency, dissuades misuse, and enables traceability.
-    # Please be a responsible AI citizen and keep the watermarking in place.
-    # If using CSM 1B in another application, use your own private key and keep it secret.
-    audio_tensor, wm_sample_rate = watermark(
-        generator._watermarker, audio_tensor, generator.sample_rate, CSM_1B_HF_WATERMARK
-    )
-    audio_tensor = torchaudio.functional.resample(
-        audio_tensor, orig_freq=wm_sample_rate, new_freq=generator.sample_rate
-    )
-
-    audio_array = (audio_tensor * 32768).to(torch.int16).cpu().numpy()
-
-    return generator.sample_rate, audio_array
-
-
-def prepare_prompt(text: str, speaker: int, audio_path: str) -> Segment:
-    audio_tensor, _ = load_prompt_audio(audio_path)
-    return Segment(text=text, speaker=speaker, audio=audio_tensor)
-
-
-def load_prompt_audio(audio_path: str) -> torch.Tensor:
-    audio_tensor, sample_rate = torchaudio.load(audio_path)
-    if audio_tensor.shape[0] != 1:
-        gr.Warning("Warning: Audio prompt is multi-channel, converting to mono.", duration=15)
-        audio_tensor = audio_tensor.mean(dim=0)
-    audio_tensor = audio_tensor.squeeze(0)
-    if sample_rate != generator.sample_rate:
-        audio_tensor = torchaudio.functional.resample(
-            audio_tensor, orig_freq=sample_rate, new_freq=generator.sample_rate
-        )
-    return audio_tensor, generator.sample_rate
-
-
-def create_speaker_prompt_ui(speaker_name: str):
-    speaker_dropdown = gr.Dropdown(
-        choices=list(SPEAKER_PROMPTS.keys()), label="Select a predefined speaker", value=speaker_name
-    )
-    with gr.Accordion("Or add your own voice prompt", open=False):
-        text_prompt_speaker = gr.Textbox(label="Speaker prompt", lines=4, value=SPEAKER_PROMPTS[speaker_name]["text"])
-        audio_prompt_speaker = gr.Audio(
-            label="Speaker prompt", type="filepath", value=SPEAKER_PROMPTS[speaker_name]["audio"]
-        )
-
-    return speaker_dropdown, text_prompt_speaker, audio_prompt_speaker
-
-
-with gr.Blocks() as app:
-    gr.Markdown(SPACE_INTRO_TEXT)
-    gr.Markdown("## Voices")
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Speaker A")
-            speaker_a_dropdown, text_prompt_speaker_a, audio_prompt_speaker_a = create_speaker_prompt_ui(
-                "conversational_a"
-            )
-
-        with gr.Column():
-            gr.Markdown("### Speaker B")
-            speaker_b_dropdown, text_prompt_speaker_b, audio_prompt_speaker_b = create_speaker_prompt_ui(
-                "conversational_b"
-            )
-
-    def update_audio(speaker):
-        if speaker in SPEAKER_PROMPTS:
-            return SPEAKER_PROMPTS[speaker]["audio"]
-        return None
-
-    def update_text(speaker):
-        if speaker in SPEAKER_PROMPTS:
-            return SPEAKER_PROMPTS[speaker]["text"]
-        return None
-
-    speaker_a_dropdown.change(fn=update_audio, inputs=[speaker_a_dropdown], outputs=[audio_prompt_speaker_a])
-    speaker_b_dropdown.change(fn=update_audio, inputs=[speaker_b_dropdown], outputs=[audio_prompt_speaker_b])
-
-    speaker_a_dropdown.change(fn=update_text, inputs=[speaker_a_dropdown], outputs=[text_prompt_speaker_a])
-    speaker_b_dropdown.change(fn=update_text, inputs=[speaker_b_dropdown], outputs=[text_prompt_speaker_b])
-
-    gr.Markdown(CONVO_INTRO_TEXT)
-
-    gen_conversation_input = gr.TextArea(label="conversation", lines=20, value=DEFAULT_CONVERSATION)
-    generate_btn = gr.Button("Generate conversation", variant="primary")
-    gr.Markdown("GPU time limited to 3 minutes, for longer usage duplicate the space.")
-    audio_output = gr.Audio(label="Synthesized audio")
-
-    generate_btn.click(
-        infer,
-        inputs=[
-            text_prompt_speaker_a,
-            text_prompt_speaker_b,
-            audio_prompt_speaker_a,
-            audio_prompt_speaker_b,
-            gen_conversation_input,
-        ],
-        outputs=[audio_output],
-    )
-
-app.launch(ssr_mode=True,mcp_server=True)