Update with streaming input

Dockerfile

@@ -53,4 +53,8 @@ ENV PYTHONPATH=${HOME}/app \
     GRADIO_SERVER_NAME=0.0.0.0 \
     GRADIO_THEME=huggingface \
     SYSTEM=spaces
-CMD ["python", "app.py"]
+
+# gradio instead of python for reload on file save with mountin pwd volume:
+# docker run -p 7860:7860 -v $(pwd):/home/user/app seamless_m4t_text
+CMD ["gradio", "app.py"]
+# CMD ["python", "app.py"]

app.py

@@ -8,428 +8,75 @@ import torch
 import torchaudio
 from seamless_communication.models.inference.translator import Translator
 
-from lang_list import (
-    LANGUAGE_NAME_TO_CODE,
-    S2ST_TARGET_LANGUAGE_NAMES,
-    S2TT_TARGET_LANGUAGE_NAMES,
-    T2TT_TARGET_LANGUAGE_NAMES,
-    TEXT_SOURCE_LANGUAGE_NAMES,
-)
+from transformers import pipeline
 
-DESCRIPTION = """# SeamlessM4T
+p = pipeline("automatic-speech-recognition")
 
-[SeamlessM4T](https://github.com/facebookresearch/seamless_communication) is designed to provide high-quality
-translation, allowing people from different linguistic communities to communicate effortlessly through speech and text.
+from pydub import AudioSegment
+import time
+from time import sleep
 
-This unified model enables multiple tasks like Speech-to-Speech (S2ST), Speech-to-Text (S2TT), Text-to-Speech (T2ST)
-translation and more, without relying on multiple separate models.
-"""
 
-CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1"
+def transcribe(audio, state=""):
+    # sleep(2)
+    print('state', state)
+    text = p(audio)["text"]
+    state += text + " "
+    return state
 
-TASK_NAMES = [
-    "S2ST (Speech to Speech translation)",
-    "S2TT (Speech to Text translation)",
-    "T2ST (Text to Speech translation)",
-    "T2TT (Text to Text translation)",
-    "ASR (Automatic Speech Recognition)",
-]
-AUDIO_SAMPLE_RATE = 16000.0
-MAX_INPUT_AUDIO_LENGTH = 60  # in seconds
-DEFAULT_TARGET_LANGUAGE = "French"
+def blocks():
+    with gr.Blocks() as demo:
+        total_audio_bytes_state = gr.State(bytes())
+        total_text_state = gr.State("")
 
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-translator = Translator(
-    model_name_or_card="seamlessM4T_large",
-    vocoder_name_or_card="vocoder_36langs",
-    device=device,
-    dtype=torch.float16,
-)
-
-
-def predict(
-    task_name: str,
-    audio_source: str,
-    input_audio_mic: str | None,
-    input_audio_file: str | None,
-    input_text: str | None,
-    source_language: str | None,
-    target_language: str,
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    task_name = task_name.split()[0]
-    source_language_code = LANGUAGE_NAME_TO_CODE[source_language] if source_language else None
-    target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
-
-    if task_name in ["S2ST", "S2TT", "ASR"]:
-        if audio_source == "microphone":
-            input_data = input_audio_mic
-        else:
-            input_data = input_audio_file
-
-        arr, org_sr = torchaudio.load(input_data)
-        new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE)
-        max_length = int(MAX_INPUT_AUDIO_LENGTH * AUDIO_SAMPLE_RATE)
-        if new_arr.shape[1] > max_length:
-            new_arr = new_arr[:, :max_length]
-            gr.Warning(f"Input audio is too long. Only the first {MAX_INPUT_AUDIO_LENGTH} seconds is used.")
-        torchaudio.save(input_data, new_arr, sample_rate=int(AUDIO_SAMPLE_RATE))
-    else:
-        input_data = input_text
-    text_out, wav, sr = translator.predict(
-        input=input_data,
-        task_str=task_name,
-        tgt_lang=target_language_code,
-        src_lang=source_language_code,
-        ngram_filtering=True,
-        sample_rate=AUDIO_SAMPLE_RATE,
-    )
-    if task_name in ["S2ST", "T2ST"]:
-        return (sr, wav.cpu().detach().numpy()), text_out
-    else:
-        return None, text_out
-
-
-def process_s2st_example(input_audio_file: str, target_language: str) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return predict(
-        task_name="S2ST",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def process_s2tt_example(input_audio_file: str, target_language: str) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return predict(
-        task_name="S2TT",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def process_t2st_example(
-    input_text: str, source_language: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return predict(
-        task_name="T2ST",
-        audio_source="",
-        input_audio_mic=None,
-        input_audio_file=None,
-        input_text=input_text,
-        source_language=source_language,
-        target_language=target_language,
-    )
-
-
-def process_t2tt_example(
-    input_text: str, source_language: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return predict(
-        task_name="T2TT",
-        audio_source="",
-        input_audio_mic=None,
-        input_audio_file=None,
-        input_text=input_text,
-        source_language=source_language,
-        target_language=target_language,
-    )
-
-
-def process_asr_example(input_audio_file: str, target_language: str) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return predict(
-        task_name="ASR",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def update_audio_ui(audio_source: str) -> tuple[dict, dict]:
-    mic = audio_source == "microphone"
-    return (
-        gr.update(visible=mic, value=None),  # input_audio_mic
-        gr.update(visible=not mic, value=None),  # input_audio_file
-    )
+        # input_audio = gr.Audio(label="Input Audio", type="filepath", format="mp3")
+        input_audio = gr.Audio(label="Input Audio", type="filepath", format="mp3", source="microphone", streaming=True)
+        with gr.Row():
+            with gr.Column():
+                stream_as_bytes_btn = gr.Button("Stream as Bytes")
+                stream_as_bytes_output = gr.Audio(format="bytes", streaming=True)
+                stream_output_text = gr.Textbox(label="Translated text")
 
 
-def update_input_ui(task_name: str) -> tuple[dict, dict, dict, dict]:
-    task_name = task_name.split()[0]
-    if task_name == "S2ST":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True, choices=S2ST_TARGET_LANGUAGE_NAMES, value=DEFAULT_TARGET_LANGUAGE
-            ),  # target_language
-        )
-    elif task_name == "S2TT":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True, choices=S2TT_TARGET_LANGUAGE_NAMES, value=DEFAULT_TARGET_LANGUAGE
-            ),  # target_language
-        )
-    elif task_name == "T2ST":
-        return (
-            gr.update(visible=False),  # audio_box
-            gr.update(visible=True),  # input_text
-            gr.update(visible=True),  # source_language
-            gr.update(
-                visible=True, choices=S2ST_TARGET_LANGUAGE_NAMES, value=DEFAULT_TARGET_LANGUAGE
-            ),  # target_language
-        )
-    elif task_name == "T2TT":
-        return (
-            gr.update(visible=False),  # audio_box
-            gr.update(visible=True),  # input_text
-            gr.update(visible=True),  # source_language
-            gr.update(
-                visible=True, choices=T2TT_TARGET_LANGUAGE_NAMES, value=DEFAULT_TARGET_LANGUAGE
-            ),  # target_language
-        )
-    elif task_name == "ASR":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True, choices=S2TT_TARGET_LANGUAGE_NAMES, value=DEFAULT_TARGET_LANGUAGE
-            ),  # target_language
-        )
-    else:
-        raise ValueError(f"Unknown task: {task_name}")
+                def stream_bytes(audio_file, total_audio_bytes_state, total_text_state):
+                    chunk_size = 30000
 
+                    print(f"audio_file {audio_file}, size {os.path.getsize(audio_file)}")
+                    with open(audio_file, "rb") as f:
 
-def update_output_ui(task_name: str) -> tuple[dict, dict]:
-    task_name = task_name.split()[0]
-    if task_name in ["S2ST", "T2ST"]:
-        return (
-            gr.update(visible=True, value=None),  # output_audio
-            gr.update(value=None),  # output_text
-        )
-    elif task_name in ["S2TT", "T2TT", "ASR"]:
-        return (
-            gr.update(visible=False, value=None),  # output_audio
-            gr.update(value=None),  # output_text
-        )
-    else:
-        raise ValueError(f"Unknown task: {task_name}")
+                        while True:
+                            chunk = f.read(chunk_size)
+                            if chunk:
+                                total_audio_bytes_state += chunk
+                                print('yielding chunk', len(chunk))
+                                print('total audio bytes', len(total_audio_bytes_state))
+                                print(f"Text state: {total_text_state}")
 
+                                # This does the whole thing every time
+                                # total_text = transcribe(chunk, "")
+                                # yield total_audio_bytes_state, total_text, total_audio_bytes_state, total_text_state
 
-def update_example_ui(task_name: str) -> tuple[dict, dict, dict, dict, dict]:
-    task_name = task_name.split()[0]
-    return (
-        gr.update(visible=task_name == "S2ST"),  # s2st_example_row
-        gr.update(visible=task_name == "S2TT"),  # s2tt_example_row
-        gr.update(visible=task_name == "T2ST"),  # t2st_example_row
-        gr.update(visible=task_name == "T2TT"),  # t2tt_example_row
-        gr.update(visible=task_name == "ASR"),  # asr_example_row
-    )
+                                # This translates just the new part every time
+                                total_text_state = transcribe(chunk, total_text_state)
+                                total_text = total_text_state
+                                # total_text = transcribe(chunk, total_text)
+                                yield total_audio_bytes_state, total_text, total_audio_bytes_state, total_text_state
+                                # sleep(3)
+                            else:
+                                break
+                def clear():
+                    print('clearing')
+                    return [bytes(), ""]
 
+                stream_as_bytes_btn.click(stream_bytes, [input_audio, total_audio_bytes_state, total_text_state], [stream_as_bytes_output, stream_output_text, total_audio_bytes_state, total_text_state])
 
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Group():
-        task_name = gr.Dropdown(
-            label="Task",
-            choices=TASK_NAMES,
-            value=TASK_NAMES[0],
-        )
-        with gr.Row():
-            source_language = gr.Dropdown(
-                label="Source language",
-                choices=TEXT_SOURCE_LANGUAGE_NAMES,
-                value="English",
-                visible=False,
-            )
-            target_language = gr.Dropdown(
-                label="Target language",
-                choices=S2ST_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            )
-        with gr.Row() as audio_box:
-            audio_source = gr.Radio(
-                label="Audio source",
-                choices=["file", "microphone"],
-                value="file",
-            )
-            input_audio_mic = gr.Audio(
-                label="Input speech",
-                type="filepath",
-                source="microphone",
-                visible=False,
-            )
-            input_audio_file = gr.Audio(
-                label="Input speech",
-                type="filepath",
-                source="upload",
-                visible=True,
-            )
-        input_text = gr.Textbox(label="Input text", visible=False)
-        btn = gr.Button("Translate")
-        with gr.Column():
-            output_audio = gr.Audio(
-                label="Translated speech",
-                autoplay=False,
-                streaming=False,
-                type="numpy",
-            )
-            output_text = gr.Textbox(label="Translated text")
+        input_audio.change(stream_bytes, [input_audio, total_audio_bytes_state, total_text_state], [stream_as_bytes_output, stream_output_text, total_audio_bytes_state, total_text_state])
+        input_audio.clear(clear, None, [total_audio_bytes_state, total_text_state])
+        input_audio.start_recording(clear, None, [total_audio_bytes_state, total_text_state])
 
-    with gr.Row(visible=True) as s2st_example_row:
-        s2st_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "French"],
-                ["assets/sample_input.mp3", "Mandarin Chinese"],
-                ["assets/sample_input_2.mp3", "Hindi"],
-                ["assets/sample_input_2.mp3", "Spanish"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_s2st_example,
-            cache_examples=CACHE_EXAMPLES,
-        )
-    with gr.Row(visible=False) as s2tt_example_row:
-        s2tt_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "French"],
-                ["assets/sample_input.mp3", "Mandarin Chinese"],
-                ["assets/sample_input_2.mp3", "Hindi"],
-                ["assets/sample_input_2.mp3", "Spanish"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_s2tt_example,
-            cache_examples=CACHE_EXAMPLES,
-        )
-    with gr.Row(visible=False) as t2st_example_row:
-        t2st_examples = gr.Examples(
-            examples=[
-                ["My favorite animal is the elephant.", "English", "French"],
-                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Hindi",
-                ],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Spanish",
-                ],
-            ],
-            inputs=[input_text, source_language, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_t2st_example,
-            cache_examples=CACHE_EXAMPLES,
-        )
-    with gr.Row(visible=False) as t2tt_example_row:
-        t2tt_examples = gr.Examples(
-            examples=[
-                ["My favorite animal is the elephant.", "English", "French"],
-                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Hindi",
-                ],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Spanish",
-                ],
-            ],
-            inputs=[input_text, source_language, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_t2tt_example,
-            cache_examples=CACHE_EXAMPLES,
-        )
-    with gr.Row(visible=False) as asr_example_row:
-        asr_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "English"],
-                ["assets/sample_input_2.mp3", "English"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_asr_example,
-            cache_examples=CACHE_EXAMPLES,
-        )
 
-    audio_source.change(
-        fn=update_audio_ui,
-        inputs=audio_source,
-        outputs=[
-            input_audio_mic,
-            input_audio_file,
-        ],
-        queue=False,
-        api_name=False,
-    )
-    task_name.change(
-        fn=update_input_ui,
-        inputs=task_name,
-        outputs=[
-            audio_box,
-            input_text,
-            source_language,
-            target_language,
-        ],
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=update_output_ui,
-        inputs=task_name,
-        outputs=[output_audio, output_text],
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=update_example_ui,
-        inputs=task_name,
-        outputs=[
-            s2st_example_row,
-            s2tt_example_row,
-            t2st_example_row,
-            t2tt_example_row,
-            asr_example_row,
-        ],
-        queue=False,
-        api_name=False,
-    )
+    demo.queue().launch()
 
-    btn.click(
-        fn=predict,
-        inputs=[
-            task_name,
-            audio_source,
-            input_audio_mic,
-            input_audio_file,
-            input_text,
-            source_language,
-            target_language,
-        ],
-        outputs=[output_audio, output_text],
-        api_name="run",
-    )
-demo.queue(max_size=50).launch()
 
-# Linking models to the space
-# 'facebook/seamless-m4t-large'
-# 'facebook/SONAR'
+# if __name__ == "__main__":
+blocks()

requirements.txt

@@ -1,6 +1,8 @@
 fairseq2==0.1.0
 git+https://github.com/facebookresearch/seamless_communication
-gradio==3.40.1
+gradio==3.41.0
 huggingface_hub==0.16.4
 torch==2.0.1
 torchaudio==2.0.2
+transformers==4.32.1
+pydub