Add Gradio Codes

.gitignore

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+.DS_Store
+sherpa-ncnn-pruned-transducer-stateless7-streaming-id/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

app.py

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+import librosa
+import sherpa_ncnn
+import os
+import time
+import gradio as gr
+import numpy as np
+
+from functools import lru_cache
+from pathlib import Path
+from huggingface_hub import Repository
+
+AUTH_TOKEN = os.getenv("AUTH_TOKEN")
+
+language_to_models = {
+    "id": [
+        "bookbot/sherpa-ncnn-pruned-transducer-stateless7-streaming-id",
+    ],
+}
+
+language_choices = list(language_to_models.keys())
+
+streaming_recognizer = None
+
+
+def recognize(
+    language: str,
+    repo_id: str,
+    decoding_method: str,
+    num_active_paths: int,
+    in_filename: str,
+):
+    recognizer = get_pretrained_model(
+        repo_id,
+        decoding_method=decoding_method,
+        num_active_paths=num_active_paths,
+    )
+
+    audio, sr = librosa.load(in_filename, sr=16_000)
+    samples_per_read = int(0.32 * sr)
+    recognized_text = ""
+
+    for i in range(0, len(audio), samples_per_read):
+        chunk = audio[i : i + samples_per_read]
+        recognizer.accept_waveform(sr, chunk)
+        transcript = recognizer.text
+        if transcript:
+            recognized_text = transcript
+
+    tail_paddings = np.zeros(int(recognizer.sample_rate * 0.5), dtype=np.float32)
+    recognizer.accept_waveform(recognizer.sample_rate, tail_paddings)
+
+    recognizer.input_finished()
+    transcript = recognizer.text
+    if transcript:
+        recognized_text = transcript
+
+    return recognized_text
+
+
+def initialize_streaming_model(
+    repo_id: str, decoding_method: str, num_active_paths: int
+):
+    streaming_recognizer = get_pretrained_model(
+        repo_id, decoding_method, num_active_paths
+    )
+    print("Re-intialized model!")
+
+
+@lru_cache(maxsize=10)
+def get_pretrained_model(repo_id: str, decoding_method: str, num_active_paths: int):
+    model_name = Path(repo_id.split("/")[-1])
+    _ = Repository(
+        local_dir=model_name,
+        clone_from=repo_id,
+        token=AUTH_TOKEN,
+    )
+
+    return sherpa_ncnn.Recognizer(
+        tokens=str(model_name / "tokens.txt"),
+        encoder_param=str(model_name / "encoder_jit_trace-pnnx.ncnn.param"),
+        encoder_bin=str(model_name / "encoder_jit_trace-pnnx.ncnn.bin"),
+        decoder_param=str(model_name / "decoder_jit_trace-pnnx.ncnn.param"),
+        decoder_bin=str(model_name / "decoder_jit_trace-pnnx.ncnn.bin"),
+        joiner_param=str(model_name / "joiner_jit_trace-pnnx.ncnn.param"),
+        joiner_bin=str(model_name / "joiner_jit_trace-pnnx.ncnn.bin"),
+        num_threads=os.cpu_count(),
+        decoding_method=decoding_method,
+        num_active_paths=num_active_paths,
+        enable_endpoint_detection=True,
+        rule1_min_trailing_silence=30,
+        rule2_min_trailing_silence=30,
+        rule3_min_utterance_length=30,
+    )
+
+
+def process_uploaded_file(
+    language: str,
+    repo_id: str,
+    decoding_method: str,
+    num_active_paths: int,
+    in_filename: str,
+):
+    return recognize(
+        in_filename=in_filename,
+        language=language,
+        repo_id=repo_id,
+        decoding_method=decoding_method,
+        num_active_paths=num_active_paths,
+    )
+
+
+def recognize_audio_from_mic(
+    in_filename: str,
+    state: str,
+):
+    audio, sr = librosa.load(in_filename, sr=16_000)
+    streaming_recognizer.accept_waveform(sr, audio)
+    time.sleep(0.32)
+    transcript = streaming_recognizer.text
+    if transcript:
+        state = transcript
+    return state, state
+
+
+def update_model_dropdown(language: str):
+    if language in language_to_models:
+        choices = language_to_models[language]
+        return gr.Dropdown.update(choices=choices, value=choices[0])
+    raise ValueError(f"Unsupported language: {language}")
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Automatic Speech Recognition with Next-gen Kaldi")
+
+    language_radio = gr.Radio(
+        label="Language", choices=language_choices, value=language_choices[0]
+    )
+    model_dropdown = gr.Dropdown(
+        choices=language_to_models[language_choices[0]],
+        label="Select a model",
+        value=language_to_models[language_choices[0]][0],
+    )
+
+    language_radio.change(
+        update_model_dropdown,
+        inputs=language_radio,
+        outputs=model_dropdown,
+    )
+
+    decoding_method_radio = gr.Radio(
+        label="Decoding method",
+        choices=["greedy_search", "modified_beam_search"],
+        value="greedy_search",
+    )
+
+    num_active_paths_slider = gr.Slider(
+        minimum=1,
+        value=4,
+        step=1,
+        label="Number of active paths for modified_beam_search",
+    )
+
+    with gr.Tab("File Upload"):
+        uploaded_file = gr.Audio(
+            source="upload",  # Choose between "microphone", "upload"
+            type="filepath",
+            label="Upload audio file",
+        )
+        uploaded_output = gr.Textbox(label="Recognized speech from uploaded file")
+        with gr.Row():
+            upload_button = gr.Button("Recognize audio")
+            upload_clear_button = gr.ClearButton(
+                components=[uploaded_file, uploaded_output]
+            )
+
+    with gr.Tab("Real-time Microphone Recognition"):
+        if streaming_recognizer is None:
+            streaming_recognizer = get_pretrained_model(
+                model_dropdown.value,
+                decoding_method_radio.value,
+                num_active_paths_slider.value,
+            )
+            print("Model initialized!")
+
+        model_dropdown.change(
+            fn=initialize_streaming_model,
+            inputs=[
+                model_dropdown,
+                decoding_method_radio,
+                num_active_paths_slider,
+            ],
+        )
+
+        state = gr.State(value="")
+        mic_input_audio = gr.Audio(
+            source="microphone",
+            type="filepath",
+            label="Upload audio file",
+        )
+        mic_text_output = gr.Textbox(label="Recognized speech from microphone")
+        mic_input_audio.stream(
+            fn=recognize_audio_from_mic,
+            inputs=[mic_input_audio, state],
+            outputs=[mic_text_output, state],
+            show_progress=False,
+        )
+        with gr.Row():
+            file_clear_button = gr.ClearButton(
+                components=[mic_text_output, state]
+            ).click(streaming_recognizer.reset)
+
+    upload_button.click(
+        process_uploaded_file,
+        inputs=[
+            language_radio,
+            model_dropdown,
+            decoding_method_radio,
+            num_active_paths_slider,
+            uploaded_file,
+        ],
+        outputs=uploaded_output,
+    )
+
+
+demo.launch(debug=True)

packages.txt

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+ffmpeg

requirements.txt

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+sherpa-ncnn
+soundfile
+librosa