update whisper stream

.github/pylint.conf

@@ -84,7 +84,7 @@ persistent=yes
 
 # Minimum Python version to use for version dependent checks. Will default to
 # the version used to run pylint.
-py-version=3.9
+py-version=3.11
 
 # Discover python modules and packages in the file system subtree.
 recursive=no

.github/workflows/ci_pipeline.yaml

@@ -140,7 +140,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest]
-        python: [3.8]
+        python: [3.9]
     runs-on: ${{ matrix.os }}
     steps:
     - uses: actions/checkout@v3
@@ -170,10 +170,10 @@ jobs:
     if: github.event_name == 'push' && github.repository_owner == 'mindspore-lab'
     steps:
     - uses: actions/checkout@v3
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v4
       with:
-        python-version: 3.8
+        python-version: 3.9
     - uses: "lvyufeng/action-kaggle-gpu-test@latest"
       with:
         kaggle_username: "${{ secrets.KAGGLE_USERNAME }}"

.github/workflows/doc_rst_check.yaml

@@ -17,7 +17,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: 3.8
+        python-version: 3.9
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip==24.0

.gitignore

@@ -163,4 +163,7 @@ data*/
 
 fusion_result.json
 aclinit.json
-xiyouji.txt
+xiyouji.txt
+*.safetensors
+*.jit
+flagged/

llm/inference/whisper/app_realtime.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import time
+import numpy as np
+import mindspore
+from mindnlp.transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+ms_dtype = mindspore.float16
+MODEL_NAME = "openai/whisper-large-v3-turbo"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    MODEL_NAME, ms_dtype=ms_dtype, low_cpu_mem_usage=True
+)
+
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    ms_dtype=ms_dtype,
+)
+
+prompt = "以下是普通话的句子。" # must have periods
+prompt_ids = processor.get_prompt_ids(prompt, return_tensors="ms")
+generate_kwargs = {"prompt_ids": prompt_ids}
+
+def transcribe(inputs, previous_transcription):
+    start_time = time.time() 
+    try:
+        sample_rate, audio_data = inputs
+        audio_data = audio_data.astype(np.float32)
+        audio_data /= np.max(np.abs(audio_data))
+
+        transcription = pipe({"sampling_rate": sample_rate, "raw": audio_data}, generate_kwargs=generate_kwargs)["text"]
+        previous_transcription += transcription
+
+        end_time = time.time()
+        latency = end_time - start_time
+        return previous_transcription, f"{latency:.2f}"
+    except Exception as e:
+        print(f"Error during Transcription: {e}")
+        return previous_transcription, "Error"
+
+
+def clear():
+    return ""
+
+with gr.Blocks() as microphone:
+    with gr.Column():
+        gr.Markdown(f"# Realtime Whisper Large V3 Turbo: \n Transcribe Audio in Realtime. This Demo uses the Checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers.\n Note: The first token takes about 5 seconds. After that, it works flawlessly.")
+        with gr.Row():
+            input_audio_microphone = gr.Audio(streaming=True)
+            output = gr.Textbox(label="Transcription", value="")
+            latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
+        with gr.Row():
+            clear_button = gr.Button("Clear Output")
+
+        input_audio_microphone.stream(transcribe, [input_audio_microphone, output], [output, latency_textbox], time_limit=45, stream_every=2, concurrency_limit=None)
+        clear_button.click(clear, outputs=[output])
+
+with gr.Blocks() as file:
+    with gr.Column():
+        gr.Markdown(f"# Realtime Whisper Large V3 Turbo: \n Transcribe Audio in Realtime. This Demo uses the Checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers.\n Note: The first token takes about 5 seconds. After that, it works flawlessly.")
+        with gr.Row():
+            input_audio_microphone = gr.Audio(sources="upload", type="numpy")
+            output = gr.Textbox(label="Transcription", value="")
+            latency_textbox = gr.Textbox(label="Latency (seconds)", value="0.0", scale=0)
+        with gr.Row():
+            submit_button = gr.Button("Submit")
+            clear_button = gr.Button("Clear Output")
+
+        submit_button.click(transcribe, [input_audio_microphone, output], [output, latency_textbox], concurrency_limit=None)
+        clear_button.click(clear, outputs=[output])
+
+with gr.Blocks(theme=gr.themes.Ocean()) as demo:
+    gr.TabbedInterface([microphone, file], ["Microphone", "Transcribe from file"])
+
+demo.launch()

llm/inference/whisper/app_stream.py

@@ -1,50 +1,94 @@
+import queue
+import threading
+import time
+
 import gradio as gr
 import numpy as np
 import mindspore
-from mindnlp.transformers import pipeline
-
+from mindspore.dataset.audio import Resample
+from mindnlp.transformers import pipeline, AutoProcessor
+from silero_vad_mindspore import load
 
 MODEL_NAME = "openai/whisper-large-v3"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
+THRESH_HOLD = 0.5
 
+stream_queue = queue.Queue()
 
+vad_model = load('silero_vad_v4')
+
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
     ms_dtype=mindspore.float16
 )
 
+prompt = "以下是普通话的句子。" # must have periods
+prompt_ids = processor.get_prompt_ids(prompt, return_tensors="ms")
+
+text = ""
+silence_count = 0
+
+resample = Resample(48000, 16000)
+generate_kwargs = {"language": "zh", "task": "transcribe", "prompt_ids": prompt_ids}
+                #    "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "no_speech_threshold": 0.5, "logprob_threshold": -1.0}
+
+# warm up
+random_sample = np.random.randn(16000).astype(np.float32)
+vad_model(mindspore.tensor(random_sample), 16000)
+pipe(random_sample, generate_kwargs=generate_kwargs, return_timestamps='word')
+
+def pipeline_consumer():
+    global text
+    while True:
+        chunk = stream_queue.get()
+        # print(speech_score)
+        genreated_text = pipe(chunk, generate_kwargs=generate_kwargs, return_timestamps='word')["text"]
+        text += genreated_text + '\n'
+
+        stream_queue.task_done()
+
+        if stream_queue.empty() and stream_queue.unfinished_tasks == 0:
+            time.sleep(1)
+
 
 def transcribe(stream, new_chunk):
-    generate_kwargs = {"language": "zh", "task": "transcribe"}
+    global text
 
     sr, y = new_chunk
+
     y = y.astype(np.float32)
     y /= np.max(np.abs(y))
-    print(y)
+    # print('sample shape:', y.shape)
+    speech_score = vad_model(mindspore.tensor(y), sr)
+    speech_score = speech_score.item()
+    print('speech socre', speech_score)
 
-    if stream is not None:
-        stream = np.concatenate([stream, y])
-    else:
-        stream = y
+    if speech_score > 0.5:
+        if stream is not None:
+            if stream.shape < y.shape or (stream[-len(y):] - y).sum() != 0:
+                stream = np.concatenate([stream, y])
+        else:
+            stream = y
 
-    if stream.shape[0] < (3 * 48000):
-        return stream, None
-
-    text = pipe({"sampling_rate": sr, "raw": y}, generate_kwargs=generate_kwargs)["text"]
-
-    if str(text).endswith((".", "。", '?', "？", '!', "！", ":", "：")):
+    if stream is not None and stream.shape[0] >= (48000 * 5): # 5s if continue talk
+        print('stream shape:', stream.shape)
+        stream_queue.put({"sampling_rate": sr, "raw": stream})
         stream = None
+
     return stream, text  # type: ignore
 
+input_audio = gr.Audio(sources=["microphone"], streaming=True)
 demo = gr.Interface(
     transcribe,
-    ["state", gr.Audio(sources=["microphone"], streaming=True)],
+    ["state", input_audio],
     ["state", "text"],
     live=True,
 )
 
 if __name__ == "__main__":
+    c = threading.Thread(target=pipeline_consumer)
+    c.start()
     demo.launch()

mindnlp/core/nn/modules/_utils.py

@@ -5,7 +5,7 @@
 
 def _ntuple(n):
     def parse(x):
-        if isinstance(x, collections.Iterable):
+        if isinstance(x, collections.abc.Iterable):
             return x
         return tuple(repeat(x, n))
     return parse