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\u001b[32m56.9/56.9 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m61.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.1/72.1 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m69.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for gruut (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for encodec (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for bnnumerizer (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gruut-ipa (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gruut_lang_de (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gruut_lang_en (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gruut_lang_es (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for gruut_lang_fr (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "albucore 0.0.19 requires numpy>=1.24.4, but you have numpy 1.22.0 which is incompatible.\n", "albumentations 1.4.20 requires numpy>=1.24.4, but you have numpy 1.22.0 which is incompatible.\n", "arviz 0.20.0 requires numpy>=1.23.0, but you have numpy 1.22.0 which is incompatible.\n", "astropy 6.1.7 requires numpy>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "bigframes 1.27.0 requires numpy>=1.24.0, but you have numpy 1.22.0 which is incompatible.\n", "chex 0.1.87 requires numpy>=1.24.1, but you have numpy 1.22.0 which is incompatible.\n", "cudf-cu12 24.10.1 requires numpy<3.0a0,>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "cudf-cu12 24.10.1 requires pandas<2.2.3dev0,>=2.0, but you have pandas 1.5.3 which is incompatible.\n", "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 1.5.3 which is incompatible.\n", "ibis-framework 9.2.0 requires numpy<3,>=1.23.2, but you have numpy 1.22.0 which is incompatible.\n", "jax 0.4.33 requires numpy>=1.24, but you have numpy 1.22.0 which is incompatible.\n", "jaxlib 0.4.33 requires numpy>=1.24, but you have numpy 1.22.0 which is incompatible.\n", "langchain 0.3.10 requires numpy<2,>=1.22.4; python_version < \"3.12\", but you have numpy 1.22.0 which is incompatible.\n", "mizani 0.13.0 requires numpy>=1.23.5, but you have numpy 1.22.0 which is incompatible.\n", "mizani 0.13.0 requires pandas>=2.2.0, but you have pandas 1.5.3 which is incompatible.\n", "numexpr 2.10.2 requires numpy>=1.23.0, but you have numpy 1.22.0 which is incompatible.\n", "nx-cugraph-cu12 24.10.0 requires networkx>=3.0, but you have networkx 2.8.8 which is incompatible.\n", "nx-cugraph-cu12 24.10.0 requires numpy<3.0a0,>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "pandas-stubs 2.2.2.240909 requires numpy>=1.23.5, but you have numpy 1.22.0 which is incompatible.\n", "plotnine 0.14.3 requires numpy>=1.23.5, but you have numpy 1.22.0 which is incompatible.\n", "plotnine 0.14.3 requires pandas>=2.2.0, but you have pandas 1.5.3 which is incompatible.\n", "pylibraft-cu12 24.10.0 requires numpy<3.0a0,>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "rmm-cu12 24.10.0 requires numpy<3.0a0,>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "scikit-image 0.24.0 requires numpy>=1.23, but you have numpy 1.22.0 which is incompatible.\n", "statsmodels 0.14.4 requires numpy<3,>=1.22.3, but you have numpy 1.22.0 which is incompatible.\n", "tensorflow 2.17.1 requires numpy<2.0.0,>=1.23.5; python_version <= \"3.11\", but you have numpy 1.22.0 which is incompatible.\n", "xarray 2024.10.0 requires numpy>=1.24, but you have numpy 1.22.0 which is incompatible.\n", "xarray 2024.10.0 requires pandas>=2.1, but you have pandas 1.5.3 which is incompatible.\n", "xarray-einstats 0.8.0 requires numpy>=1.23, but you have numpy 1.22.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[2K 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"!pip install whisper -q\n", "!pip install openai-whisper -q" ] }, { "cell_type": "code", "source": [ "!pip install diffusers -q" ], "metadata": { "id": "C3HO7VPnYGON" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "import torch\n", "from diffusers import StableDiffusionPipeline\n", "from TTS.api import TTS\n", "import whisper\n", "from huggingface_hub import login\n", "\n", "# Log in to Hugging Face using your token\n", "hf_token = \"hf_token\" # Replace with your Hugging Face token\n", "login(hf_token)\n", "\n", "# **Text-to-Image Initialization**\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "text_to_image_pipeline = StableDiffusionPipeline.from_pretrained(\n", " \"runwayml/stable-diffusion-v1-5\",\n", " torch_dtype=torch.float16 if device == \"cuda\" else torch.float32,\n", " token=hf_token\n", ").to(device)\n", "\n", "def generate_image(prompt):\n", " \"\"\"Generate an image from text using Stable Diffusion.\"\"\"\n", " try:\n", " result = text_to_image_pipeline(prompt)\n", " image = result.images[0] # Extract the first generated image\n", " return image\n", " except Exception as e:\n", " return f\"Error generating image: {str(e)}\"\n", "\n", "# **Speech-to-Text and Text-to-Speech Initialization**\n", "# Initialize models\n", "tts = TTS(model_name=\"tts_models/en/ljspeech/tacotron2-DDC\",\n", " progress_bar=True,\n", " gpu=torch.cuda.is_available())\n", "whisper_model = whisper.load_model(\"base\")\n", "\n", "def transcribe_audio(audio_file):\n", " \"\"\"Transcribe the uploaded audio file.\"\"\"\n", " try:\n", " result = whisper_model.transcribe(audio_file)\n", " return result[\"text\"]\n", " except Exception as e:\n", " return f\"Error in transcription: {str(e)}\"\n", "\n", "def process_voice(audio_file, text_to_speak=None, transcribe=False):\n", " \"\"\"Process voice and generate speech.\"\"\"\n", " try:\n", " # Transcribe if requested\n", " transcription = None\n", " if transcribe:\n", " transcription = transcribe_audio(audio_file)\n", "\n", " # Use transcription as text to speak if no text is provided\n", " if text_to_speak is None or text_to_speak.strip() == \"\":\n", " text_to_speak = transcription if transcription else \"Hello, this is a test of voice synthesis.\"\n", "\n", " # Generate speech\n", " output_path = \"output_speech.wav\"\n", " tts.tts_to_file(text=text_to_speak, file_path=output_path)\n", "\n", " return output_path, transcription if transcribe else \"Voice synthesis successful!\"\n", " except Exception as e:\n", " return None, f\"Error: {str(e)}\"\n", "\n", "# **Combine Interfaces in Tabs**\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"## Unified Gradio App for Text-to-Image and Speech Processing\")\n", "\n", " with gr.Tab(\"Text-to-Image\"):\n", " gr.Markdown(\"### Generate Images from Text\")\n", " text_to_image_prompt = gr.Textbox(label=\"Enter your prompt\", placeholder=\"Describe the image you want...\")\n", " text_to_image_output = gr.Image(label=\"Generated Image\", type=\"pil\")\n", " text_to_image_button = gr.Button(\"Generate Image\")\n", " text_to_image_button.click(generate_image, inputs=text_to_image_prompt, outputs=text_to_image_output)\n", "\n", " with gr.Tab(\"Speech Processing\"):\n", " gr.Markdown(\"### Speech-to-Text and Text-to-Speech\")\n", " audio_input = gr.Audio(type=\"filepath\", label=\"Upload voice sample (WAV file)\")\n", " tts_text_input = gr.Textbox(label=\"Text to speak (optional if transcribing)\")\n", " transcribe_checkbox = gr.Checkbox(label=\"Transcribe input audio\")\n", " audio_output = gr.Audio(label=\"Synthesized Voice Output\")\n", " transcription_output = gr.Textbox(label=\"Transcription/Status\")\n", " process_button = gr.Button(\"Process Voice\")\n", " process_button.click(process_voice,\n", " inputs=[audio_input, tts_text_input, transcribe_checkbox],\n", " outputs=[audio_output, transcription_output])\n", "\n", "# Launch the app\n", 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"d8d677de7f3f4564a773032ba87b4e66", "32c415a0b4334626bdb35f704b4443d2", "032c028278ae4ba5915a78abc54720d8", "69a4ebedd1a0465aa8598bdb51b6c4be", "b99a4e4a887d4c90a29cb91929c16f79", "3822372b9a3a48ee84eac8639d477dc2", "141c05d8deae462491ac24227f4517c8", "a38cbaec3d9340b1b6290879694a4a7b", "164ea653f691465c9daf87a1bc1ea440", "03437d10433348b39b3b804e542673ca", "aa109c985a904ef48e8b66f17fe6595c", "5753864d44b949b8a6cc4982afce606b" ] }, "id": "3cOlSjHkX0In", "outputId": "7c0687cd-7ed4-4111-ff6f-a763acf7de36" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "0it [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "96f6660fbbd14095ab24cf93a5472370" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "model_index.json: 0%| | 0.00/541 [00:00 Downloading model to /root/.local/share/tts/tts_models--en--ljspeech--tacotron2-DDC\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ " 95%|█████████▍| 107M/113M [00:01<00:00, 74.8MiB/s] " ] }, { "output_type": "stream", "name": "stdout", "text": [ " > Model's license - apache 2.0\n", " > Check https://choosealicense.com/licenses/apache-2.0/ for more info.\n", " > Downloading model to /root/.local/share/tts/vocoder_models--en--ljspeech--hifigan_v2\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "100%|██████████| 113M/113M [00:02<00:00, 39.4MiB/s]\n", "\n", " 59%|█████▊ | 2.23M/3.80M [00:00<00:00, 22.3MiB/s]\u001b[A" ] }, { "output_type": "stream", "name": "stdout", "text": [ " > Model's license - apache 2.0\n", " > Check https://choosealicense.com/licenses/apache-2.0/ for more info.\n", " > Using model: Tacotron2\n", " > Setting up Audio Processor...\n", " | > sample_rate:22050\n", " | > resample:False\n", " | > num_mels:80\n", " | > log_func:np.log\n", " | > min_level_db:-100\n", " | > frame_shift_ms:None\n", " | > frame_length_ms:None\n", " | > ref_level_db:20\n", " | > fft_size:1024\n", " | > power:1.5\n", " | > preemphasis:0.0\n", " | > griffin_lim_iters:60\n", " | > signal_norm:False\n", " | > symmetric_norm:True\n", " | > mel_fmin:0\n", " | > mel_fmax:8000.0\n", " | > pitch_fmin:1.0\n", " | > pitch_fmax:640.0\n", " | > spec_gain:1.0\n", " | > stft_pad_mode:reflect\n", " | > max_norm:4.0\n", " | > clip_norm:True\n", " | > do_trim_silence:True\n", " | > trim_db:60\n", " | > do_sound_norm:False\n", " | > do_amp_to_db_linear:True\n", " | > do_amp_to_db_mel:True\n", " | > do_rms_norm:False\n", " | > db_level:None\n", " | > stats_path:None\n", " | > base:2.718281828459045\n", " | > hop_length:256\n", " | > win_length:1024\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/TTS/utils/io.py:54: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " return torch.load(f, map_location=map_location, **kwargs)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ " > Model's reduction rate `r` is set to: 1\n", " > Vocoder Model: hifigan\n", " > Setting up Audio Processor...\n", " | > sample_rate:22050\n", " | > resample:False\n", " | > num_mels:80\n", " | > log_func:np.log\n", " | > min_level_db:-100\n", " | > frame_shift_ms:None\n", " | > frame_length_ms:None\n", " | > ref_level_db:20\n", " | > fft_size:1024\n", " | > power:1.5\n", " | > preemphasis:0.0\n", " | > griffin_lim_iters:60\n", " | > signal_norm:False\n", " | > symmetric_norm:True\n", " | > mel_fmin:0\n", " | > mel_fmax:8000.0\n", " | > pitch_fmin:1.0\n", " | > pitch_fmax:640.0\n", " | > spec_gain:1.0\n", " | > stft_pad_mode:reflect\n", " | > max_norm:4.0\n", " | > clip_norm:True\n", " | > do_trim_silence:False\n", " | > trim_db:60\n", " | > do_sound_norm:False\n", " | > do_amp_to_db_linear:True\n", " | > do_amp_to_db_mel:True\n", " | > do_rms_norm:False\n", " | > db_level:None\n", " | > stats_path:None\n", " | > base:2.718281828459045\n", " | > hop_length:256\n", " | > win_length:1024\n", " > Generator Model: hifigan_generator\n", " > Discriminator Model: hifigan_discriminator\n", "Removing weight norm...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|████████████████████████████████████████| 139M/139M [00:01<00:00, 129MiB/s]\n", "/usr/local/lib/python3.10/dist-packages/whisper/__init__.py:150: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " checkpoint = torch.load(fp, map_location=device)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "* Running on public URL: https://12134fc9035cb81cb0.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "wN36A3qWYxFB" }, "execution_count": null, "outputs": [] } ] }